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# CHARMM: The Biomolecular Simulation Program

^{1,}

^{*}C.L. Brooks, III,

^{2,}

^{*}A.D. MacKerell, Jr.,

^{3,}

^{*}L. Nilsson,

^{4,}

^{*}R.J. Petrella,

^{5,}

^{6,}

^{*}B. Roux,

^{7,}

^{*}Y. Won,

^{8,}

^{*}G. Archontis, C. Bartels, S. Boresch, A. Caflisch, L. Caves, Q. Cui, A.R. Dinner, M. Feig, S. Fischer, J. Gao, M. Hodoscek, W. Im, K. Kuczera, T. Lazaridis, J. Ma, V. Ovchinnikov, E. Paci, R.W. Pastor, C.B. Post, J.Z. Pu, M. Schaefer, B. Tidor, R. M. Venable, H. L. Woodcock, X. Wu, W. Yang, D.M. York, and M. Karplus

^{5,}

^{9,}

^{*}

^{1}Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892

^{2}Departments of Chemistry & Biophysics, University of Michigan, Ann Arbor, MI 48109

^{3}Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, 21201

^{4}Karolinska Institutet, Department of Biosciences and Nutrition, SE-141 57, Huddinge, Sweden

^{5}Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138

^{6}Department of Medicine, Harvard Medical School, Boston, MA 02115

^{7}Department of Biochemistry and Molecular Biology, University of Chicago, Gordon Center for Integrative Science, Chicago, IL 60637

^{8}Department of Chemistry, Hanyang University, Seoul 133–792 Korea

^{9}Laboratoire de Chimie Biophysique, ISIS, Université de Strasbourg, 67000 Strasbourg France

## Abstract

CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. In addition, the CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This paper provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM paper in 1983.

**Keywords:**biomolecular simulation, CHARMM program, molecular mechanics, molecular dynamics, molecular modeling, biophysical computation, energy function

## I. Introduction

Understanding how biological macromolecular systems (proteins, nucleic acids,
lipid membranes, carbohydrates, and their complexes) function is a major objective
of current research by computational chemists and biophysicists. The hypothesis
underlying computational models of biological macromolecules is that the behavior of
such systems can be described in terms of the basic physical principles governing
the interactions and motions of their elementary atomic constituents. The models
are, thus, rooted in the fundamental laws of physics and chemistry, including
electrostatics, quantum mechanics and statistical mechanics. The challenge now is in
the development and application of methods, based on such well-established
principles, to shed light on the structure, function, and properties of
often-complex biomolecular systems. With the advent of computers, the scope of
molecular dynamics (MD; see footnote for naming conventions)^{*} and other simulation techniques has evolved
from the study of simple hard-sphere models of liquids in the
1950’s,^{1} to that of models of
more complex atomic and molecular liquids in the 1960’s,^{2}^{,}^{3} and to the
study of proteins in the 1970’s.^{4}
Biological macromolecular systems of increasing size and complexity, including
nucleic acids, viruses, membrane proteins, and macromolecular assemblies, are now
being investigated using these computational methods.

The power and usefulness of atomic models based on realistic microscopic
interactions for investigating the properties of a wide variety of biomolecules, as
well as other chemical systems, has been amply demonstrated. The methodology and
applications have been described in numerous books^{5}^{–}^{10} and
reviews.^{11}^{–}^{13} Studies of such systems have now reached a point where
computational models often have an important role in the design and interpretation
of experiments. Of particular interest is the possibility of employing molecular
simulations to obtain information that is difficult to determine
experimentally.^{14}^{,}^{15} A dictionary definition of
“simulation” is, in fact, “the examination of a
problem, often not subject to direct experimentation,” and it is this
broad meaning that is intended here. Typical studies range from those concerned with
the structures, energies, and vibrational frequencies of small molecules, through
those dealing with Monte Carlo and molecular dynamics simulations of pure liquids
and solutions, to analyses of the conformational energies and fluctuations of large
molecules in solution or in crystal environments.

As the field of biomolecular computation continues to evolve, it is essential
to retain maximum flexibility and to have available a wide range of computational
methods for the implementation of novel ideas in research and its applications. The
need to have an integrated approach for the development and application of such
computational biophysical methods has led to the introduction of a number of
general-purpose programs, some of which are widely distributed in academic and
commercial environments. Several^{16}^{–}^{21} were described
in a special 2005 issue of *Journal of Computational Chemistry*. One
of the programs, CHARMM (Chemistry at HARvard Molecular Mechanics), was not included
in that publication because a paper was not prepared in time for the issue. CHARMM
was first described in *JCC* in 1983,^{22} although its earlier implementation had already been used to study
biomolecules for a number of years.^{23}

CHARMM is a general and flexible molecular simulation and modeling program
that uses classical (empirical and semiempirical) and quantum mechanical
(semiempirical or *ab initio*) energy functions for molecular systems
of many different classes, sizes, and levels of heterogeneity and complexity. The
original version of the program, although considerably smaller and more limited than
CHARMM is at present, made it possible to build the system of interest, optimize the
configuration using energy minimization techniques, perform a normal mode or
molecular dynamics simulation, and analyze the simulation results to determine
structural, equilibrium, and dynamic properties. This version of CHARMM^{24} was able to treat isolated molecules,
molecules in solution, and molecules in crystalline solids. The information for
computations on proteins, nucleic acids, prosthetic groups (e.g., heme groups), and
substrates was available as part of the program. A large set of analysis facilities
was provided, which included static structure and energy comparisons, time series,
correlation functions and statistical properties of molecular dynamic trajectories,
and interfaces to computer graphics programs. Over the years, CHARMM has been ported
to many different machines and platforms, in both serial and parallel
implementations of the code; and it has been made to run efficiently on many types
of computer systems, from single-processor PCs, Mac and Linux workstations, to
machines based on vectorial or multi-core processors, to distributed-memory clusters
of Linux machines, and large, shared-memory super-computer installations. Equally
important, the structure of the program has provided a robust framework for
incorporating new ideas and methodologies — many of which did not even
exist when CHARMM was first designed and coded in the late 1970’s. Some
examples are implicit solvent representations, free energy perturbation methods,
structure refinement based on X-ray or NMR data, transition path sampling, locally
enhanced sampling with multiple copies, discretized Feynman path integral
simulations, quantum mechanical/molecular mechanical (QM/MM) simulations, and the
treatment of induced polarization. The ability of the basic framework of CHARMM to
accommodate new methods without large-scale restructuring of the code is one of the
major reasons for the continuing success of the program as a vehicle for the
development of computational molecular biophysics.

The primary goal of this paper is to provide an overview of CHARMM as it
exists today, focusing on the developments of the program during the 25 years since
the publication of the first paper describing the CHARMM program in 1983^{22}. In addition, the current paper briefly
reviews the origin of the program, its management, its distribution to a broad group
of users, and future directions in its development. Some familiarity with the
original CHARMM paper is assumed. Although many details of CHARMM usage, such as
input commands and options, are included, full documentation is available on-line at
www.charmm.org, as well as with all distributions of the program.
The present work also provides, *de facto*, a review of the current
state of the art in computational molecular biophysics. Consequently, it should be
of interest not only to the CHARMM user community, but also to scientists employing
other programs.

## II. Overview of the Program

The central motivation for creating and developing the molecular simulation program CHARMM is to provide an integrated environment that includes a wide range of tools for the theoretical investigation of complex macromolecular systems, with particular emphasis on those that are important in biology. To achieve this, the program is self-contained and has been designed to be versatile, extensible, portable and efficient. CHARMM strikes a balance between general efficiency (the ability of the end user to easily set up, run, and analyze a project) and extensibility/versatility (the ability of the program to support new implementations and the use of many methods and approaches). This section provides an introduction to some general aspects of the CHARMM program and its use, including the essential elements of a typical CHARMM project. In what follows, detailed descriptions are given of most of the program’s features.

### A) Outline of a Generic CHARMM Project

A typical research project with CHARMM can be described in very general
terms based on the information flow in the program, which is schematically
illustrated in Figure 1. The user begins a
project by first setting up the atomic model representing the system of interest
(see also Section IX A). This consists of importing the
“residue” topologies file (RTF) and force field
parameters (PRM), generating the “protein” structure
file (PSF), and assembling a complete configuration (coordinates) of all the
atoms in the system; the quotes around “residue” and
“protein” indicate that the same (historical) notation
is used when the program is applied to molecules in general. For molecules and
moieties that have been parameterized, such as proteins, nucleic acids, and
lipids, standard CHARMM PRM and RTF files can be used, and the setup procedure
is straightforward if most of the coordinates are known. For molecules not
included in the standard libraries, CHARMM is designed to allow for the use of a
virtually unlimited variety of additional molecular topologies and force field
parameters. (The available force fields are discussed in Section III.) For
calculations involving multiple copies of a structure, such as reaction path
calculations in which the coordinates of the two end structures are derived from
x-ray crystallographic data, consistency of atom labels is required across all
of the copies, particularly for chemically equivalent atoms (e.g.
Cδ1 and Cδ2 of Tyr). CHARMM provides a set of general
tools for facilitating the setup and manipulation of the molecular system (e.g.,
coordinate transformations and the construction of missing coordinates; Sections
IX B and C) and for imposing a variety of constraints (Section V B) and
restraints (Section III F) on the system, where appropriate; restraints allow
changes in the property of interest with an energetic penalty, while constraints
fix the property, usually to user-specified values. The user can specify a
number of options for the calculation of non-bonded interactions and can choose
to impose any of a number of boundary conditions on the system (Section IV). To
carry out the calculations in an acceptable length of real time, the user must
consider tradeoffs in accuracy/complexity *versus* efficiency
(Section XII) when selecting the model to be employed in the calculations; in
addition, he or she may need to use a parallel compilation of the code or to
utilize time-saving features such as lookup tables (Section X). There are
currently two web-based interface utilities that can be used to facilitate the
setup phase of a CHARMM project, CHARMM-GUI^{25} and CHARMMing.^{26}

^{nd}row trapezoid) is first used to fill

**...**

The project may require a preproduction stage: e.g., for a molecular dynamics simulation, the usual procedure is to minimize the system structure (often obtained from crystallographic or NMR data), to heat the system to the desired temperature, and then to equilibrate it. Once this is done, the project enters the production stage, during which the atomic conformation of the system may be refined, explored, and sampled by the application of various computational procedures. These procedures may consist, among other possibilities, of performing energy minimization, propagating molecular dynamics or Langevin dynamics trajectories, sampling with Metropolis Monte Carlo or grid-based search algorithms, obtaining thermodynamic free energy differences via free energy perturbation computations, performing transition path sampling, or calculating normal modes of vibrations. With such methodologies, it is possible to simulate the time evolution of the molecular system, optimize and generate conformations according to various statistical mechanical ensembles, characterize collective motions, and explore the energy landscape along particular reaction pathways. Some computational techniques (e.g., so-called “alchemical” free energy simulations) include the consideration of “unphysical” intermediate states to improve the calculation of physical observables, including the free energy, entropy and enthalpy change due to a mutation or conformational transition. These algorithms and methods, which are central to many theoretical studies of biological macromolecules and other mesoscopic systems, are discussed in Sections V, VI, and VII. Although several key quantities are normally monitored during the production stage of a project, additional system properties may have to be determined by post-processing the data—e.g., to calculate free energy changes from the coordinates or diffusion coefficients from the velocities saved during one or more molecular dynamics trajectories. These derived quantities, whose calculation is described in Section VIII, may include time-series, correlation functions, or other properties related to experimental observables. Finally, the advanced CHARMM user in some cases will have extended the program’s functionality in the course of carrying out his project, either by creating CHARMM scripts (Section II C), writing external code as an adjunct, utilizing internal “hooks” to the CHARMM source code (Section IX A), or directly modifying one or more source code modules. After such developmental code has been made to conform to CHARMM coding standards and tested, it should be submitted to the CHARMM manager so as to be considered for inclusion in future distributions of the program (Section XI).

### B) Functional Multiplicity of CHARMM

An important feature of CHARMM is that many specific computational tasks
(e.g., the calculation of a free energy or the determination of a reaction
pathway) can be accomplished in more than one way. This diversity has two major
functions. First, the best method to use often depends on the specific nature of
the problem being studied. Second, within a given type of problem or method, the
level of approximation that achieves the best balance between accuracy
requirements and computational resources often depends on the system size and
complexity. A typical example arises in the class of models that are used to
represent the effect of the surrounding solvent on a macromolecule. The most
realistic representation treats the solvent environment by explicitly including
the water molecules (as well as any counter-ions, crystal neighbors or membrane
lipids, if they are present), and imposing periodic boundary conditions (PBC),
which mimic an infinite system by reproducing the central cell^{7}^{,}^{8} (see
Section IV B). Systems varying from tens to even hundreds of thousands of
particles can be simulated with such all-explicit-atom models for hundreds of
nanoseconds using currently available computational resources, such as large,
distributed-memory clusters of nodes and parallel program architectures.
However, a drawback of treating solvated systems in this way is that most of the
computing time (often over 90%) is used for simulating the solvent
rather than the parts of the system of primary interest. Consequently, an
alternative approach is often used in which the influence of the solvent is
incorporated implicitly with an effective mean-field potential (i.e., without
the inclusion of actual water molecules in the calculation). This approach can
greatly reduce the computational cost of a calculation for a protein relative to
the use of explicit solvent, often by a hundred-fold or more, and captures many
of the equilibrium properties of the solvent. However, it introduces
approximations, so that hydrodynamic and frictional solvent effects, as well as
the role of water structure, are usually not accounted for in the implicit
solvent approach. A variety of implicit solvent models, with differing accuracy
and efficiency profiles, are available in CHARMM; a detailed discussion can be
found in Section III D. An intermediate approach between all-atom PBC
simulations and implicit solvent models involves simulating only a small region
explicitly in the presence of a reduced number of explicit solvent molecules,
while applying an effective solvent boundary potential (SBP) to mimic the
average influence of the surrounding solvent.^{27}^{–}^{29} The SBP
approach is often advantageous in simulations requiring an explicit, atomic
representation of water in a limited region of the system—for
example, in the study of a reaction taking place in the active site of a large
enzyme.^{30} The choice of solvent
representation for a project thus depends on several factors, including the
accuracy requirements of the calculation, the type of data being sought, the
system size, and the computational resources and (real) time available.

### C) The CHARMM Scripting Language

Although CHARMM can be run interactively, as is often done when the
CHARMM graphics facility (GRAPHX) is being used, intensive computational
projects are normally executed in batch mode through the use of input files
(Figure 2). A set of command
structures, including *GOTO*, *STREam*, and
*IF*-*ELSE*-*ENDIf* structures,
corresponding to the respective control-flow statements in source code, provide
the basis for a powerful high-level scripting language that permits the general
and flexible control of complicated simulation protocols and facilitates the
prototyping of new methods. The various functionalities of CHARMM can easily be
combined in almost any way using these command structures in scripts to satisfy
the requirements of a particular project. In general, the order of CHARMM
commands is limited only by the data required by the command. For example, the
energy cannot be calculated unless the arrays holding the coordinates,
parameters, and structural topology, etc., have already been filled (Figure 1). The command parser allows the
substitution of numerous variables, which are set either internally by the
program during execution (for example, the current number of atoms is accessible
as “?natom”), or externally by the user (for example, a
user may initially issue the command “*SET*
temperature 298.15”, and then substitute its value as
“@temperature” on any command line in the CHARMM input
script). All components of the most recent energy evaluation, as well as the
results of many other calculations, are available as internal CHARMM variables
(?identifier). The numerical values for the variables can then be written to an
external file, further processed, or used in control statements
(“*IF* ?ener .lt. −500
*THEN*…”). Arrays of these variables
can also be constructed (e.g., “segid1”,
“segid2”, …,
“segid10”) and referenced
(“@segid@@j”). The parser has a robust interpreter of
arithmetic expressions (*CALC*), which can be used to evaluate
algebraic functions of these variables using basic mathematical operations,
including random number generation. Variable values may also be passed to the
program at the start of execution. In addition, it is possible to call other
CHARMM scripts as subroutines (*STREam* …
*RETUrn*), and to access operating system commands
(*SYSTem*); depending on the operating system, CHARMM can use
environment variables in filenames. In addition, the *SCALar*
command facility performs arithmetic and statistical manipulations on internal
CHARMM vectors (e.g., coordinates, forces, charges, masses, user-defined
arrays). CHARMM variables and arrays can be read from (*GET, SCALar
READ*) or written to (*ECHO, WRITe TITLe, SCALar
WRITe*) external files, with or without header information,
allowing, for example, easy access from external graphing programs. The extent
of printing can be controlled with the *PRNLevel* and
*WRNLevel* commands, which take integers in the range of
−10 (print no messages or warnings) to +11 (print all).
In general, values larger than 5 (default) will result in output that is not
needed for production calculations but may be useful for debugging and
script-checking purposes. For example, *PRNLevel* 8 will print
the name of every energy-based subroutine as it is called.

^{4}The example

**...**

Since CHARMM input files can take the form of mini-programs written in
the interpretive language of CHARMM commands, common tasks can be coded in a
general way at the script level. As examples, standard input scripts have been
written for the addition of explicit solvent to a system, and a series of
scripts has been developed that automates the setup of the initial configuration
for a membrane-protein molecular dynamics simulation (Figure 3).^{31}^{–}^{33} It is also
possible to implement complex methods and simulation protocols at the level of
the input file without changing the source code. For example, the Random
Expulsion method^{34} has been implemented
in this way in a study of ligand escape from a nuclear receptor^{35} (Figure 4);
see also Blondel *et al*.^{36} Another example is the development and parameterization of a
coarse-grained model of an amphipathic polypeptide which was used to investigate
the kinetics of amyloid aggregation.^{37}
The flexibility of the scripting language is such that one could implement
Metropolis Monte Carlo sampling in a few lines directly from the input files
(though this would run less efficiently than the dedicated MC module). In
addition, the scripting language is used extensively when performing the
calculations required for the optimization of force field parameters (see next
section).

**...**

## III. Atomic Potential Energy Function

The relationship between structure and energy is an essential element of many computational studies based on detailed atomic models. The potential energy function, by custom called a force field, is used to calculate the potential energy of the system and its derivatives from the coordinates corresponding to the structure or conformation. It has two aspects: the mathematical form and the empirical parameters. In CHARMM, the topology (RTF) and parameter (PRM) files (see Figure 1), along with the polymer sequence, allow the potential energy function to be fully defined. First derivatives of the potential energy are used to determine the atomic forces, which are required for molecular dynamics simulation and energy minimization. Second derivatives of the potential energy, which are required for the calculation of vibrational spectra and for some energy minimization algorithms, are also available. In a program like CHARMM, which is undergoing continuous development, changes in the force field and the rest of the code are often linked and developments in both made in concert.

Because force fields are approximations to the exact potential energy, they
are expected to improve over time. The goals of force field development involve at
least three factors; they are accuracy, breadth, and speed. Accuracy can be defined
as the extent to which calculations using a force field can reproduce experimental
observables. Breadth refers to the range of moieties, molecules, and systems to
which a force field can be applied at the required level of accuracy. Speed is the
relative efficiency of calculations using one force field over another, all else
being equal; this often depends largely on the level of detail of the models,
although the form of implementation can also have a role. In addition, the
introduction of improvements to a *given* force field must be
balanced by the need for stability of the force field (i.e. constancy of the form
and parameters) over time. This is particularly true of accuracy gains: while
improved accuracy in a given force field may be desired, continual change would make
comparison of results from different versions of the force field problematic. In
CHARMM, there have been continual force field developments over the years, many of
which are discussed, including the development of force fields based on more
detailed atomic representations (e.g all atom, polarizable) and applicability to
more molecular types (e.g. DNA, carbohydrates, lipids). At the same time, an effort
has been made not to change validated and well-tested force fields, thereby
facilitating comparison of results from studies performed at different times and in
different laboratories. Notably, the only modification to the protein part of the
all-atom fixed-point-charge CHARMM force field^{38} since May 1993 has been the addition of a dihedral correction term
(see Section III. C below, CMAP); the nucleic acid part of this force field^{39}^{–}^{41} has remained unchanged since 1998.

### A) Molecular mechanics force fields

The general form of the potential energy function most commonly used in
CHARMM for macromolecular simulations is based on fixed point charges, is shown
in Eq. (1) (see also Brooks
*et al.*^{22} and Section
IX. A).

The potential energy,
(*U*(*$\stackrel{\u20d7}{R}$*)), is a sum over
individual terms representing the internal and non-bonded contributions as a
function of the atomic coordinates. Internal terms include bond
(*b*), valence angle (*θ*),
Urey-Bradley (UB, *S*), dihedral angle
(*ϕ*), improper angle
(*ω*), and backbone torsional correction
(CMAP,*ϕ,ψ*) contributions, as shown
in Eq. (1).^{22}^{,}^{41a}^{,}^{72} The parameters *K _{b,}
K_{ϕ}*,

*K*,

_{UB}*K*and

_{θ}*K*are the respective force constants and the variables with the subscript 0 are the respective equilibrium values. All the internal terms are taken to be harmonic, except the dihedral angle term, which is a sinusoidal expression; here

_{ω}*n*is the multiplicity or periodicity of the dihedral angle and

*δ*is the phase shift. The all-atom implementations of the CHARMM force field include all possible valence and dihedral angles for bonded atoms, and the dihedral angle term about a given bond may be expanded in a Fourier series of up to six terms. Most commonly, one dihedral angle term is used, though 2 or more have been introduced in some cases. In addition, for the protein main chain a numerical correction term, called CMAP, has been implemented (see below). For three bonded atoms A-B-C, the Urey-Bradley term is a quadratic function of the distance,

*S*, between atoms A and C. The improper dihedral angle term is used at branchpoints; that is, for atoms A, B, and D bonded to a central atom, C, the term is a quadratic function of the (pseudo)dihedral angle defined by A-B-C-D. Both the Urey-Bradley and improper dihedral terms are used to optimize the fit to vibrational spectra and out-of-plane motions. In the polar hydrogen models (models in which CH3, CH2 and CH groups are treated as single extended atoms; see below), the improper dihedral angle term is also required to prevent inversion of chirality (e.g., about the C

_{α}atom in proteins). While the improper dihedral term is used very generally in the CHARMM force fields, the Urey-Bradley term tends to be used only in special cases.

Non-bonded terms include Coulombic interactions between the point
charges (*q _{i}* and

*q*) and the Lennard-Jones (LJ) 6–12 term, which is used for the treatment of the core-core repulsion and the attractive van der Waals dispersion interaction. Non-bonded interactions are calculated between all atom pairs within a user-specified interatomic cutoff distance, except for covalently bonded atom pairs (1,2 interactions) and atom pairs separated by two covalent bonds (1,3 interactions). The relative dielectric constant,

_{j}*ε*, is set to one in calculations with explicit solvent, corresponding to the permittivity of vacuum,

*ε*

_{0}. In addition, the electrostatic term can be scaled using other values for the dielectric constant or a distance-dependent dielectric; in the latter, the electrostatic term is inversely proportional to ${r}_{ij}^{2}$, the distance between the interacting atoms squared. Expressions for

*ε*used for implicit solvent model calculations are discussed in Section III D. CHARMM also contains an explicit hydrogen bonding term, which is not used in the current generation of CHARMM force fields, but remains as a supported energy term for the purposes of facilitating model development and hydrogen bonding analysis.

^{42}In the LJ term, the well depth is represented by ${\epsilon}_{ij}^{min}$, where

*i*and

*j*are the indices of the interacting atoms,

*r*is the interatomic distance, and ${R}_{ij}^{min}$ is the distance at which the LJ term has its minimum. Typically, ${\epsilon}_{ii}^{min}$ and ${R}_{i}^{min}$ are obtained for individual atom types and then combined to yield ${\epsilon}_{ij}^{min}$ and ${R}_{ij}^{min}$ for the interacting atoms via a standard combination rule. In the current CHARMM force fields the ${\epsilon}_{ij}^{min}$ values are obtained via the geometric mean ( ${\epsilon}_{ij}^{min}=\sqrt{{\epsilon}_{ii}^{min}{\epsilon}_{jj}^{min}}$) and ${R}_{ij}^{min}$ via the arithmetic mean, ${R}_{ij}^{min}=({R}_{i}^{min}+{R}_{j}^{min})/2$. Other LJ combining rules are also supported, e.g., ${R}_{ij}^{min}=\sqrt{{R}_{i}^{min}{R}_{j}^{min}}$, allowing for the use of alternative force fields in CHARMM (see below). Separate LJ parameters and a scaling factor for electrostatics can be used for the non-bonded interactions between atoms separated by three covalent bonds (1,4 interactions). The Buckingham potential

_{ij}^{43}has recently been added as an alternative to the simple LJ for treating the core repulsion. The Morse potential,

^{44}often used for bond-breaking, is also implemented.

The simple form for the potential energy used in Eq. (1) represents a compromise between
accuracy and speed. For biomolecules at or near room temperature, the harmonic
representation is generally adequate, though approximate, and the same holds
true for the use of the Lennard-Jones potential for the van der Waals
interactions. However, alternative force fields, with additional correction
terms, are available in CHARMM (Section III B) and can be used to check the
results obtained with Eq. (1).
The earliest force field in CHARMM was based on an extended-atom (united atom)
model, in which no hydrogen atoms were included explicitly. The omitted
hydrogens were treated instead as part of the atom to which they were
bonded.^{45}^{,}^{46} These “extended atom”
force fields typically required the explicit hydrogen bonding term mentioned
above. A significant advance beyond the early models was based on the finding
that the distance and angle dependencies of hydrogen bonds could be treated
accurately by the LJ and electrostatic terms alone if the so-called polar
hydrogens (OH and NH) were treated explicitly.^{47} This eliminated the need for the inclusion of explicit hydrogen
bonding terms and led to the creation of PARAM19,^{48} called “the polar hydrogen model” for
simulations of proteins. This model, which was first developed in the mid
1980’s^{47} is still widely
used, particularly in simulations of proteins with an implicit treatment of the
solvent (Section III D).

All-atom representations are the basis of the present generation of
CHARMM force fields and were designed for simulations with explicit solvent. In
these force fields an effort was made to optimize the parameters using model
compounds representative of moieties comprised by the macromolecules.^{49} Testing was done against a variety of
experimentally determined structural and thermodynamic properties of model
compounds and macromolecules, augmented by quantum mechanical calculations. A
balance of polar interactions (e.g., hydrogen bonds) between protein-protein,
protein-water, and water-water interactions was maintained in the
parameterization. CHARMM uses a slightly modified form of the TIP3P water
model,^{50} which includes LJ-parameters
for the hydrogens as well as the oxygen.^{51}
^{48} The properties of the model are not
significantly altered,^{52}^{–}^{54} because the
hydrogens (*r _{min}* = 0.2245 Å)
are well inside the van der Waals spheres of the oxygens
(

*r*= 1.7682 Å, O-H bond length = 0.9572 Å). The modification was introduced to avoid singularities in the use of integral equations for representing the solvent

_{min}^{55}; it is not important for explicit-solvent molecular dynamics simulations. Currently, the all-atom models in CHARMM include the CHARMM22 force field for proteins,

^{56}the CHARMM27 force field for nucleic acids,

^{39}

^{,}

^{41}and force fields for lipids.

^{57}

^{–}

^{59}A limited set of parameters for carbohydrates is available,

^{60}with a more extensive set under development

^{61}(Brady, J.W., Pastor, R.W., MacKerell Jr., A.D., work in progress).

These force fields have been designed to be compatible, allowing for
studies of heterogeneous systems. The nucleic acid and lipid force fields are
significant improvements over earlier all-atom models produced in the
1990’s;^{62}^{,}^{63} the gains were achieved through extensive testing
with macromolecular simulations and improved quantum mechanical benchmarks.^{59} In addition, force field parameters are
available for a variety of modified protein and nucleic acid moieties and
prosthetic groups.^{41}^{,}^{64}^{,}^{65}
Moreover, a description of the appropriate methods for extending the CHARMM
all-atom force fields to new molecules or moieties has been published,^{49} and tools for carrying out this type of
extension are available via the CHARMM web page at http://www.charmm.org. The
all-atom CHARMM force fields, with a few improvements described below, have been
applied to many different systems and shown to be adequate for quantitative
studies (e.g., free energy simulations). Separately, an extended version of the
CHARMM all-atom force fields for the treatment of candidate drug-like molecules
is currently under development. Combined with a flexible parameter reader and
automated RTF generation, this “generalized” force field
will be particularly useful for screening of drug candidates (Brooks, B.R. and
MacKerell Jr., A.D., work in progress)

### B) Additional Supported Force Fields

Access to multiple highly optimized and well-tested force fields for
simulations of biological macromolecules is useful for assessing the robustness
of the computational results. In addition to the force fields developed
specifically for CHARMM, versions of the AMBER nucleic acid and protein force
fields,^{66}^{,}^{67} the OPLS protein force fields^{68} with the TIP3P or TIP4P water models,^{50}^{,}^{69} and the nucleic acid force field from Bristol-Myers Squibb^{70} have been integrated for use with other
parts of the CHARMM program. The SPC,^{71}
SPC/E^{72} and ST2^{73} water models are also available. A recent
comparison of simulations with the CHARMM22, AMBER and OPLS force fields showed
that the three models give good results that are similar for the structural
properties of three proteins.^{69} Since
that study, the CHARMM force field has been improved by adding a spline-based 2D
dihedral energy correction term (CMAP) for the protein backbone (see Section
III. C).^{74} For the free energy of
hydration of 15 amino acid side chain analogs, the CHARMM22, AMBER and OPLS
force fields yielded comparable deviations (of about 1 kcal/mol) from the
experimental values.^{75}^{,}^{76} A simulation of the conformational dynamics of the
eight principal deoxyribo- and ribonucleosides using long explicit-solvent
simulations showed that the CHARMM27 force field yields a description in
agreement with experiment and provides an especially accurate representation of
the ribose moiety.^{77} This study also
details a comparison of simulations using the CHARMM27 and AMBER nucleic acid
force fields, performed with CHARMM. A simulation study described by Reddy
*et al.*^{78} compares
the different force fields available in CHARMM for B-DNA oligomers. In addition,
CHARMM has been shown to yield quantitative agreement with NMR imino proton
exchange experiments on base opening.^{79}^{–}^{81}

CHARMM also includes the Merck Molecular Force Field (MMFF)^{82}^{,}^{83} and the Consistent Force Field (CFF).^{84}^{,}^{85} These
force fields use so-called “Class II” potential energy
functions that differ from that in Eq.
(1) by the addition of cross terms between different internal
coordinates (e.g., terms that couple the bond lengths and angles) and
alternative methods for the treatment of the non-bonded interactions. The CFF
force field is based on the early force field of Lifson and Warshel.^{86} The MMFF force field is specifically
designed to be used within the CHARMM program for the study of a wide range of
organic compounds of pharmaceutical interest. CHARMM is able to read PDB, MERCK
or MOL2 formatted files, including MOL2 databases, so as to support large-scale
virtual drug screening. Also, a script is available that transforms the MMFF
parameterization for a given molecule so as to be consistent with the standard
CHARMM force field.

### C) Recent Extensions and Current Developments

#### Improved Backbone Dihedral Angle Potential

An important advance for the accurate calculation of the internal
energies of biomolecules is the introduction of a multi-dimensional spline
fitting procedure.^{74}^{,}^{87} It allows for any target energy surface
associated with two dihedral angles to be added to the potential energy
function in Eq. (1). The use
of the spline function, referred to as CMAP, corrects certain small
systematic errors in the description of the protein backbone by the all-atom
CHARMM force field. The CMAP correction, which is based on *ab
initio* QM calculations, as well as structure-based potentials
of mean force, significantly improves the structural and dynamic results
obtained with molecular dynamics simulations of proteins in crystalline and
solution environments.^{74}^{,}^{88} Additional simulations have shown
improved agreement with N-H order parameters as measured by NMR.^{89} The spline function is expected to
be generally useful for improving the representation of the internal
flexibility of biopolymers when the available data indicate that corrections
are required.^{90}

#### Treatment of Induced Polarization

A refinement in the fixed charge distribution of the standard CHARMM
biomolecular force field is the incorporation of the influence of induced
electronic polarization. Polarization is expected to have particularly
important effects on the structure, energetics, and dynamics of systems
containing charged (e.g., metal ions) or highly polar species. There is also
an indication that polarization effects can be significant in accurately
modeling the non-polar hydrocarbon core of lipid membranes.^{91}^{,}^{92}
Although the physics of polarization is well understood, there are problems
associated with introducing it into biomolecular simulations. They concern
the choice of a suitable mathematical representation, the design of
efficient computational algorithms, and the re-parameterization of the force
field. The three most promising representations are the fluctuating charge
model introduced by Rick and Berne,^{93}
which is based on the charge-equalization principle,^{94} the classical Drude oscillator model (also
called the Shell model),^{95} and the
induced point dipole model.^{96}^{–}^{98} Patel
and Brooks^{99} have developed and
tested a polarizable CHARMM force field for proteins based on a
charge-equalization scheme (CHEQ module). It is currently being used in
molecular simulations to explore the role of electronic polarizability in
proteins and peptides in solution,^{99}^{,}^{100} at phase
boundaries in alcohols,^{101}^{,}^{102} and alkanes,^{103} and in the conductance of ion channels.^{92} MacKerell, Roux and co-workers are
exploring a polarizable model based on the classical Drude oscillator
methods^{104} and have developed
the SWM4-DP polarizable water model,^{105}^{,}^{106} which has been
used to simulate DNA in solution.^{107}
A recent parameterization of alkanes,^{108} alcohols,^{109}^{,}^{110} aromatics,^{111} ethers,^{112} amides,^{113} and small
ions^{114} demonstrates the ability
of Drude oscillator-based polarizabilities to reproduce a set of
experimental observables that are incorrectly modeled by force fields with
fixed charges. Examples include the dielectric constants of neat
alkanes,^{108} water-ethanol
mixtures with concentrations that vary over the full molar fraction
range,^{109}^{,}^{113} and liquid N-methylacetamide, as well as the
excess concentration of large, polarizable anions found at the air-water
interface.^{115}^{–}^{118} Gao and coworkers have used
polarizable intermolecular potential functions, PIPFs, that model electronic
polarization with an induced point dipole approach to study polarization
effects in a series of organic liquids including alkanes, alcohols and
amides;^{96}^{,}^{98}^{,}^{119}
the results obtained with the induced-dipole model were found to be in good
accord with those obtained from combined QM/MM simulations in which
polarization effects were introduced with quantum mechanical
calculations.

In all three induced polarization methods, the polarization is
modeled as additional dynamical degrees of freedom that are propagated
according to extended Lagrangian algorithms. This treatment avoids the need
to introduce computationally inefficient approaches based on iterative
self-consistent field (SCF) methods.^{104}^{,}^{120} Efforts are
currently underway to obtain complete sets of protein, nucleic acid, and
lipid parameters for these polarizable force fields.

The polarizable models described here represent ongoing combined code and parameter developments that will be incorporated into the next generation of CHARMM force fields. Once this has been accomplished, it will be possible to carry out additional comparative studies (i.e., simulations with and without polarization) to determine the types of problems for which the use of such polarizable force fields is important.

### D) Implicit Solvent Methods

Although molecular dynamics simulations in which a large number of
solvent molecules are included provide the most detailed representation of a
solvated biomolecular system (see below), incorporating the influence of the
solvent implicitly via an effective mean-field potential can provide a
cost-efficient alternative that is sufficiently accurate for solving many
problems of interest. While implicit solvent simulations have computational
requirements (CPU and memory) that can be close to those for vacuum
calculations, they avoid many of the artifacts present in the latter, such as
large deviations from crystal structures, excessive numbers of salt bridges, and
fluctuations that are too small relative to crystallographic B factors. The
reduction in computer time obtained with implicit models, relative to the use of
an explicit solvent environment, can be important for problems requiring
extensive conformational searching, such as simulations of peptide and protein
folding^{121}^{–}^{123} and studies of the conformational
changes in large assemblies.^{122}^{,}^{124} Implicit solvent approaches allow the
estimation of solvation free energies while avoiding the statistical errors
associated with averages extracted from simulations with a large number of
solvent molecules. Examples of this type of approach are the MM/GBSA or MM/PBSA
approaches to approximate free energies,^{125} pK_{a} calculations for ligands in a protein
environment,^{126}^{–}^{129} and scoring protein conformations in
*ab initio* folding or homology modeling studies.^{130}^{–}^{133} An implicit solvent also permits arbitrarily
large atomic displacements of the solute without solvent clashes, leading to
more efficient conformational sampling in Monte Carlo and grid-based algorithms.
Recently developed implicit membrane models, by analogy with implicit water (or
other solvent) models, facilitate the study of proteins embedded in
membranes.^{134}^{–}^{139} Implicit solvent representations are
also useful as conceptual tools for analyzing the results of simulations
generated with explicit solvent molecules and for better understanding the
nature of solvation phenomena.^{140}^{,}^{141} Finally, the instantaneous solvent
relaxation that is inherent in implicit solvation models is useful for the study
of macromolecular conformational changes over the
“simulation-accessible” nanosecond or shorter
timescales, as in forced unfolding MD simulations of proteins,^{142}
*versus* the experimental microsecond to millisecond timescales.
Treating the solvent explicitly in this type of calculation can introduce
artifacts because of possible coupling between the solvent relaxation, which
occurs on the nanosecond timescale, and the sped-up conformational change.

Several implicit solvent approaches are available in CHARMM, which
effectively extend the number of available force fields in the program. The
implicit solvent models differ both in their theoretical framework (e.g., the
surface area-based empirical solvation potentials *versus* the
approximate continuum models based on generalized Born theory) and in their
implementation. A comparison of five of the effective (implicit solvent) free
energy surfaces for three peptides known to have stable conformations in
solution is presented by Steinbach.^{143}
Good agreement between results obtained with implicit and explicit solvent has
been observed for the potential of mean force as a function of the end-to-end
distance of a 12-residue peptide^{144} and
as a function of the radius of gyration of a 6-residue peptide.^{145} The implicit solvent methods currently available
in CHARMM are outlined below. A comparison of the speeds of several of the
methods with vacuum and explicit solvent calculations is also presented.

#### Solvent Accessible Surface Area Models

One of the earliest and simplest implicit solvent models implemented
in CHARMM, and currently the fastest one in the program, is based on the
solvent accessible surface area (SASA).^{146} Models of this kind make the assumption that the solvation
free energy of each part of a molecule is proportional to its
SASA—i.e., they approximate the contribution arising from solute
interactions with the first solvation shell by use of a term that is a sum
of all of these individual ‘self-energy’
contributions. In the original formulation by Eisenberg and coworkers,^{147}^{,}^{148} the solvation free energy term was expressed as
*G _{H}* = Σ

*H*+

_{i}f_{i}*C*, where

_{i}*H*is the hydrophobicity of an individual protein residue,

_{i}*f*is the fraction of the residue’s surface that is available to solvent, the

_{i}*C*‘s are constants, and the sum is over all residues in the molecule. The method was subsequently refined by the introduction of atomic solvation parameters (ASPs), which are the atomic analogues of the

_{i}*H*factors, and the solvation energy term was written as a sum over individual atomic contributions (without the constant terms).

_{i}^{147}

^{,}

^{148}This form of the SASA model has largely replaced the Wesson and Eisenberg formulation, although the latter is still available in CHARMM (along with a derivative form for membranes). The current CHARMM implementation of the SASA model

^{149}uses the polar hydrogen (PARAM19) potential energy, has two ASPs, calculates the solvent accessible surface area analytically

^{150}and includes approximate solvent shielding effects for the charges. One ASP value in the CHARMM SASA model is negative, favoring the direct solvation of polar groups, and the other is positive, approximating the hydrophobic effect on non-polar groups.

^{149}The two parameters were optimized to be consistent with the simplified treatment of electrostatic interactions based on the neutralization of charged groups

^{151}and the use of distance-dependent dielectric screening (with

*ε*(r)=2r). The charge neutralization and distance-dependent dielectric address, in an approximate way, solvent shielding of the electrostatic interactions that is not accounted for in the simpler SASA-based solvation models. However, in the present approach the shielding does not depend on the environment (i.e., given the same interatomic distance, a pair of charges in the interior of a protein feels the same screening as a pair of charges at the protein surface) so that it is most accurate for peptides and small proteins, where most of the atoms are on or near the surface. The change in the SASA, as a function of the system coordinates, can be used to obtain forces for minimization and dynamics. In part because the surface area calculation is analytic and based on interatomic distances, the SASA model is fast and has been shown to be useful in computationally demanding problems, such as the analysis of interactions in icosahedral viral capsids.

^{152}The two-ASP SASA model has been used for investigating the folding mechanism of structured peptides

^{153}

^{–}

^{156}and small proteins,

^{157}as well as the reversible mechanical unfolding of a helical peptide.

^{158}Moreover, simulations of the early steps of aggregation of amyloid-forming peptides using the SASA model have provided evidence of the importance of side chain interactions

^{159}

^{,}

^{160}and elucidated the role of aggregation “hot-spots” along the polypeptide sequence.

^{161}Because of the efficiency of the two-ASP SASA model,

^{149}most of the studies mentioned involved simulations of several microseconds in length, which have yielded adequate sampling of the peptide systems at equilibrium. A SASA model based on the all-atom representation is also present in CHARMM as part of the RUSH module

^{162}(see CHARMM documentation).

#### Gaussian solvation free energy model (EEF1)

A related model, referred to as Effective Energy Function 1
(EEF1),^{151} combines an
excluded-volume implicit solvation model with a modified version of the
polar hydrogen energy function (PARAM19 atomic representation). The model is
similar in spirit to SASA/ASP but does not require the calculation of the
solvent accessible surface area. In EEF1, as in the SASA/ASP model, the
solvation free energy is considered to be the sum of contributions from the
system’s constituent elements. The solvation free energy of each
group of atoms in the EEF1 model is equal to the solvation free energy that
the same group has in a reference (model) compound, minus the solvation lost
due to the presence of other protein groups around it (solvent exclusion
effect). A Gaussian function is used to describe the decay of the solvation
free energy density with distance. Group contributions to the solvation free
energy were obtained from an analysis of experimental solvation free energy
data for model compounds.^{163}^{,}^{164} In addition to the
solvent-exclusion effect, the dielectric screening of electrostatic
interactions by water is accounted for by the use of a distance-dependent
dielectric constant and the neutralization of ionic side chains; the latter
is essential for the EEF1 model, and was also adopted in the two-ASP SASA
model.^{149}^{,}^{153} MD simulations with EEF1 are about 1.7 times
slower than vacuum simulations but significantly faster than most of the
other solvation models in CHARMM (see below). The model has been tested
extensively. It yields modest deviations from crystal structures in MD
simulations at room temperature and unfolding pathways that are in
satisfactory agreement with explicit solvent simulations. The model has been
used to discriminate native conformations from misfolded decoys^{130} and to determine the folding free
energy landscape of a β-hairpin.^{165}^{,}^{166} Other studies
include the exploration of partially unfolded states of
α-lactalbumin,^{167} a
series of studies of protein unfolding,^{142}^{,}^{168}^{–}^{170} the
investigation of coupled unfolding/dissociation of the p53 tetramerization
domain,^{171} the identification of
stable building blocks in proteins,^{172} an analysis of the energy landscape of polyalanine,^{173} an analysis of the heat capacity
change upon protein denaturation,^{174}
the packing of secondary structural elements of proteins into the correct
tertiary structural folds,^{175} and
calculations of the contributions to protein-ligand binding free
energies.^{176} EEF1 has been used
by Baker and coworkers in successful protein-protein docking^{177} and protein design studies.^{178} An implicit membrane model based on EEF1 is
available in CHARMM.^{135} An updated
parameterization based on potential of mean force calculations for ionizable
side chains^{179} is referred to as
EEF1.1.^{135} EEF1 has also been
adapted for use with the all-atom CHARMM 22 energy function,^{180} but this formulation has not yet been
extensively tested.

#### Screened Coulomb Potentials Implicit Solvent Model (SCPISM)

The SCPISM continuum model uses a screened Coulomb potential to
describe solvent shielded interactions, based on the Debye theory of
liquids.^{181}^{,}^{182} In the SCPISM model, the standard
electrostatic component of the force field (Coulomb interaction *in
vacuo*) is replaced by terms that describe both the screened
electrostatic interactions and the self-energy of each atom. Hydrogen
bonding modulation^{183} and
non-electrostatic solvent-induced forces (e.g., hydrophobicity) are included
in the recent version. The current implementation in CHARMM can be used for
energy evaluations, minimization, and molecular dynamics simulations. It has
recently been shown that the SCPISM model preserves the main structural
properties of proteins (of up to 75 amino acids) in long (>35 ns)
Langevin dynamics simulations, as well as hydrogen bond patterns of residues
at the protein/solvent interface.^{88}
For a 15,000-atom system, MD simulations with this method (using an all-atom
model) are ~5 times slower than with EEF1 (which uses a polar hydrogen model
representation).

#### Implicit Solvent with Reference Integral Site Model (RISM)

The RISM module in CHARMM implements the reference interaction site
model.^{184} This is based on an
approximate statistical mechanical theory that involves the site-site
Ornstein-Zernike integral equation and makes possible the calculation of the
average solvent radial pair correlation function around a molecular solute.
The calculated site-site radial distribution functions *g(r)*
and pair correlation functions *c(r)* can then be used to
determine quantities such as the potential of mean force (PMF) between two
solvated molecules, and the excess chemical potential of solvation of a
solute in a solvent. The method was first used to characterize the effect of
solvent on the flexibility of alanine dipeptide.^{55} The change in the solvent
*g(r)* upon solvation can be determined, which allows for
the decomposition of the excess chemical potential into the energy and
entropy of solvation.^{185} Further
development would be required for the application of the method to larger
peptides and small proteins, which is now feasible given the availability of
fast computers.^{186}

#### Poisson-Boltzmann Continuum Electrostatics

The Poisson-Boltzmann equation provides the basis for the most accurate continuum models of solvation effects on electrostatic interactions. Thus, the PB models are used as the standards for other continuum models, but have the drawback that they are computationally intensive, though still less costly than the use of explicit solvent. The linearized PB equation for macroscopic continuum media has the form:

where is the electrostatic potential and
ε, κ and ρ are the spatially varying
dielectric constant, ionic screening and atomic charge density,
respectively. This formulation is based on the assumption that, at a given
position in space, the polarization density of the solvent and the local
cationic and anionic densities are linearly proportional to the local
electric field and local electrostatic potential, respectively. At
physiologic ionic strength and lower charge densities, the linear and
non-linear forms of the PB equation give equivalent results;^{187} use of the non-linear form, which is more
computationally costly, is recommended in cases where the charge density is
too high for the linear approximation to hold. This can be true at low ionic
strength for nucleic acid systems. In the CHARMM program (PBEQ module), the
PB equation is solved numerically using an iterative finite-difference
relaxation algorithm^{188}^{,}^{189} by mapping the system (i.e.,
ε, κ and ρ) onto a discrete spatial
grid. The PBEQ module can handle the linear and nonlinear forms of the PB
equation, as well as a partially linearized form inspired by the 3D-PLHNC
closure of Kovalenko and Hirata.^{190}
For the linear PB model, the electrostatic solvation free energy is
calculated as

where q_{i} is the charge on particle
*i* and
** _{rf}** (

*i*) is the reaction field at the position of particle

*i*(usually obtained by subtracting the electrostatic potential in vacuum from that calculated with the dielectric solvent environment). This can also be expressed as

^{191}

where *M*_{rf} (*i,j*)
is the reaction field Green function matrix. The PBEQ module in CHARMM^{191}^{,}^{192} computes the electrostatic potential and the solvation free
energy using this approach. The accuracy of continuum electrostatic models
is sensitive to the choice of the atomic radii used for setting the
dielectric boundary between the solute and the solvent. For accurate PB
calculations with the PBEQ module, optimized sets of atomic protein and
nucleic acid Born-like radii have been determined using molecular dynamics
simulations and free energy perturbation calculations with explicit water
molecules.^{192}^{,}^{193} Continuum electrostatic calculations with the
optimized atomic radii provide an implicit solvent approach that is
generally useful; examples are the studies of nucleic acids and their
complexes with proteins^{194}^{,}^{195} and of MM/PBSA calculations on
kinase inhibitor affinities.^{196} The
PBEQ module also has a number of features that can be used in electrostatic
calculations related to biological membranes.^{32}^{,}^{197} In particular, it
can be employed to calculate the transmembrane potential profile and the
induced capacitive surface charge corresponding to a given transmembrane
potential difference, which is essential for examining conformational
changes driven by an electrostatic voltage difference across the
membrane.^{197}^{,}^{198}

In addition to the standard Dirichlet boundary conditions (fixed
potential on the edge of the grid), a number of options for imposing
alternative boundary conditions on the edge of the finite grid are
available; they include conducting boundary conditions (zero electrostatic
potential), periodic boundary conditions in three dimensions, and planar
periodic boundary conditions in two dimensions. The latter are useful for
calculations involving planar membranes. The average electrostatic potential
over user-specified parts of the system can also be calculated
(*PBAVerage* subcommand); this is used, for example, in
charge-scaling procedures. It is also possible to use the result from a
coarse grid to set up the boundary conditions of a finer grid, focusing on a
small region of interest. The PBEQ module is not limited to the most common
applications of the finite-difference PB equation, which involve determining
the effective solvation of a solute in a given conformation. An accurate
method for calculating the analytic first derivative of the
finite-difference PB solvation free energy with respect to the atomic
coordinates of the solute (electrostatic solvation forces) has also been
implemented.^{191} It allows the
PBEQ module to be used in combination with several of the other tools
available in CHARMM for investigating the properties of biological
macromolecules (i.e., energy minimization, molecular dynamics, reaction path
optimization, normal modes, etc.). Since the PB calculation treats the
effect of solvent only on the electrostatic interactions, it is often
combined with methods for estimating the hydrophobic contribution. The
simplest one approximates the term as proportional to the solvent-accessible
surface area, but in recent years more sophisticated approaches have been
developed. For example, AGBNP in the Impact program^{199} and PBSA in Amber^{200} account for both cavity and solute-solvent
dispersion interactions.

#### Smooth “Conductor-Like Screening Model” (COSMO) Solvation Model

Solvation boundary element methods based on the COSMO^{201} model have proved to be stable and
efficient. This model relies on an electrostatic variational principle that
is exact for a conductor, and with certain corrections, provides useful,
approximate results for many solvents over a broad range of dielectric
constants.^{202}^{–}^{204}

For such a model, the solvent reaction field potential can be
represented as the potential arising from a surface charge distribution that
lies at the dielectric boundary. This allows study of a two-dimensional
surface problem instead of a three-dimensional volume problem. An advantage
is that it is often easier to refine the discretization of the
two-dimensional boundary element surface than to increase the resolution of
a three-dimensional grid in a finite-difference PB calculation. In the COSMO
approach, the numerical solution of the variational problem involves the
discretization of the cavity surface into tesserae that are used to expand
the solvent polarization density from which the reaction field potential is
derived. A difficulty that can arise in the surface discretization used in
these methods involves ensuring continuity of the solvation energy and its
derivatives with respect to the atomic coordinates, which is critical for
stable molecular mechanics optimization procedures and dynamics simulations.
The smooth COSMO method developed by York and Karplus^{205} addresses this problem and provides a stable
and efficient boundary element method solvation model that can be used in a
variety of applications. The method utilizes Gaussian surface elements to
avoid singularities in the surface element interaction matrix, and a
switching function that allows surface elements to smoothly appear or
disappear as atoms become exposed or buried. The energy surface in this
formulation has been demonstrated to have smooth analytic derivatives, and
the method has been recently integrated into the semiempirical MNDO97^{206} program interfaced with
CHARMM.^{207}^{,}^{208}

The smooth COSMO method, like the COSMO method, has some computational advantages (in both speed and memory requirements) over the PB method that arise from the discretization procedure. The convergence of the numerical solution in all three of the methods depends on the resolution of the grids, and in the case of the COSMO methods, the lower dimensionality of the grid used to discretize the numerical problem leads generally to increased computational efficiency and lower demands on computer memory. However, the COSMO methods are less general than the PB method in that the latter can treat spatially varying dielectric constants and effects of ion concentration in a more straightforward manner.

#### Generalized Born Electrostatics

Implicit solvent models based on the generalized Born (GB) formalism
share the same underlying dielectric continuum model for the solvent as the
Poisson or Poisson-Boltzmann (PB) methods. However, GB theories replace the
time-consuming iterative solution for obtaining the electrostatic potential
required in finite-difference PB calculations in Eq. (2) by the solvent-induced reaction
field energy as approximated by a pairwise sum over interacting charges,
*q _{i}*,

^{209}

^{–}

^{213}

In this expression *ε _{p}*,

*ε*are the interior and exterior dielectric constants,

_{w}*r*is the distance between atoms

_{ij}*i*and

*j*, and

*α*is the effective Born radius of atom

_{i}*i*, which is chosen to match the self-energy of charge

*i*at its position in the system (i.e.,

*α*varies with the position of the atoms). The empirical factor

*F*modulates the length-scale of the Gaussian term and typically ranges from 2 to 10, with 4 being the most commonly used value.

^{209}Eq. (5) assumes that the shielded electrostatic interactions arising in the dielectric environment can be expressed as a superposition of pairwise terms. This is the so-called “pairwise shielding approximation”. The efficiency of the GB approach lies in the possibility of estimating the effective atomic Born radii using a computationally inexpensive scheme. For example, the Coulomb field approximation assumes that the dielectric displacement for a set of charges embedded in a low dielectric cavity behaves like the Coulomb field of these charges in vacuum,

^{213}

^{,}

^{214}leading to the following expression for

*α*

_{i}

where *R _{i}* is usually the atomic
van der Waals radius of atom

*i*. Many generalized Born theories approximate the volume integral, carried out over the entire solute cavity, by a discrete sum of overlapping spheres

^{211}

^{,}

^{212}or Gaussians.

^{213}Alternative methods have also been devised to carry out the integration, with moderate computational cost, either by reformulating the volume integral into a surface integral

^{215}or by directly using analytical integration techniques borrowed from density functional theory.

^{134}

^{,}

^{216}

^{,}

^{217}

Several implicit solvent schemes based on the pairwise shielding
approximation exist in CHARMM. The first to be implemented in CHARMM was the
Analytic Continuum Electrostatics (ACE) model developed by Schaefer and
Karplus.^{213} This model is based
on the Coulomb field approximation and the pairwise summation utilizing
Gaussian functions as described above.^{213} Applications of the model include molecular dynamics
simulations and studies of the folding of proteins and peptides.^{121}^{,}^{218} An improved version of ACE, called ACE2, is now available
and should be used in most applications with the PARAM19 polar hydrogen
force field. Also implemented in CHARMM is a
“standard” GB model following the formulation of Qiu
*et al.*^{211} This
approach utilizes a pairwise sum over atoms to provide estimates of the
atomic Born radii (solution to Eq.
6 above).^{219} It is
optimized for use with the PARAM 19 polar hydrogen force field described
above, with which it yields mean-absolute errors of
1–2% in the calculated solvation energies, compared
to Poisson solutions using the same dielectric boundary. This model,
accessed in CHARMM via the *GBORn* command (GENBORN
preprocessor keyword), has been integrated with a number of other methods,
such as free energy perturbation calculations and replicas. It has proven
useful in folding studies of peptides and proteins,^{220} the investigation of helix to coil
transitions,^{221} and binding free
energy calculations.^{222}

The description of the solvent boundary at the molecular surface in
the ACE and standard GB methods can lead to problems that arise from the
presence of microscopic, solvent-inaccessible voids of high dielectric in
the interior of larger biomolecules. One approach used in PB calculations is
to fill the voids with neutral spheres of low-dielectric constant.^{223} In an alternative approach, the
integral formulation described by Eq. (6) can be evaluated numerically with methods drawn from
density functional theory.^{216} This
method can be extended with analytical approximations for the molecular
volume or a van der Waals-based surface with a smooth switching function
similar to that used by Im *et al.* in the context of the PB
equation.^{191} The molecular
volume approximation is implemented in the GBMV model,^{217} the smoothed van der Waals surface in the
GBSW model.^{134} These approaches
provide results that are comparable to “exact”
continuum Poisson theory.^{224}
However, they are considerably more time-consuming than the simpler models.
The GBSW model is approximately 5 times as expensive as corresponding vacuum
simulations, and the GBMV model is 6–10 times as expensive (see
also next subsection). The GBMV and GBSW models have been applied to
protein-ligand interactions,^{225}
protein-protein and protein-DNA interactions,^{141} pH-coupled molecular dynamics^{127}^{,}^{129}
and protein folding/scoring in structure prediction.^{132} Key in improving the accuracy of these models
have been extensions beyond the Coulomb field approximation described in
Eq. (6) above,^{216}^{,}^{217} which is exact only for a single charge at the center of a
spherical cavity.^{226} The FACTS model
(fast analytical continuum treatment of solvation) is a recently developed
GB method in which the effective Born radius of each atom is estimated
efficiently by using empirical formulas for approximating the volume and
spatial symmetry of the solvent that is displaced by its neighboring
atoms.^{227} Apart from the factor
F in Eq. (5), the GB
implementations in CHARMM involve empirical volume parameters for the
calculation of the Born radii in Eq.
(6). The ACE model uses type-dependent atomic volumes derived by
averaging over high-resolution structures in the PDB, ^{228} and a single adjustable (smoothing)
parameter. The value normally chosen for this parameter (1.3) gives the best
agreement between the solute volume description underlying
ACE—the superposition of Gaussians -and the solute cavity model
that is used in the standard finite difference PB methods.

Currently, the focus in GB developments has begun to shift away from
matching PB results and toward reproducing explicit solvent simulations and
experimental data through reparameterization of the models.^{138}^{,}^{229}
Recent examples demonstrate that the resulting class of implicit solvent
force fields can reproduce folding equilibria for both helical and
beta-hairpin peptides, as illustrated in Figure 5a for the folding of Trp-zip, a small helical
peptide.

#### Speed Comparison of Implicit Solvent Models

Since reducing the required computer time is one of the primary
reasons for the use of implicit solvent models, approximate timings obtained
for small- to medium-sized systems are given in Table 1. The fourth column lists the
computational cost for each model relative to a corresponding vacuum
calculation using the same system, cutoff distances, atomic representation,
and conditions. By this “intrinsic cost” measure,
which gives an indication of the speed of the implicit solvent term
calculation, *per se*, the implicit models are all in the
range of 1.7 to 10 times slower than vacuum. As expected, the cost of the
explicit water calculations (using periodic boundary conditions and particle
mesh Ewald summations; see Section IV B) is much greater than that of the
implicit models; i.e., explicit solvent calculations are approximately 20 to
200 times slower than the corresponding vacuum calculations, depending on
the size of the system, the number of water molecules used, and the atomic
representation used for the solute. Column 5 of the table lists the
computational cost for each model, using its recommended cutoff distances
and atomic representation, relative to a vacuum calculation on the same
system using an 8 Å cutoff and a polar hydrogen representation.
By this “actual cost” measure, which relates the
speeds of the models when they are used as recommended (default parameters),
the implicit models vary in speed by a factor of 50 or more. These
differences arise primarily from the fact that the models employ different
atomic representations (all-hydrogen vs. polar hydrogen) and non-bonded
cutoff distances (8 Å in SASA vs. up to 20 Å in the
others), in addition to having different intrinsic speeds or costs. The
polar-hydrogen model has ~2 times fewer atoms than the all-hydrogen model
for proteins, so that there are ~4 times fewer pairwise interactions in
models 1 and 2 than in models 3 to 6. The longer non-bonded cutoff distances
for models 4 to 6 mean that larger numbers of pairwise intramolecular
protein interactions are taken into account. The actual cost, rather than
the intrinsic cost, must be used to estimate the relative computer times
that will be required for calculations with the given models. For example,
MD simulations with the SASA model are up to 100–200 times
faster than explicit water simulations.

#### Implicit Membrane Models

In the same spirit as the implicit solvent (water) potentials,
implicit membrane representations reduce the required computer time by
modeling the membrane environment about a solute (often an embedded protein
or peptide) as one or more continuous distributions. Formulations based upon
either Poisson-Boltzmann theory (GB-like models)^{230} or Gaussian solvation energy density
distributions (an EEF1-type model)^{135} have been developed. The first GB/IM model was developed as
an extension of the simple two-dielectric form of the GB theory^{219} by splitting the integral in Eq. (6) into intramembrane and
extramembrane parts.^{136} This model
has been shown to reproduce the positions of helices within a biological
membrane. The introduction of a smooth switching function to describe the
solute-solvent boundary^{134} and the
reformulation of the integration schemes for Eq. (6)
^{216}^{,}^{217} have led to the introduction of a GB model that permits
arbitrarily shaped low-dielectric volumes to be
“embedded” in the high-dielectric solvent.^{231} This model has been developed in
the GBSW and GBMV modules, and it has been applied to the simulation and
folding of integral membrane peptides and proteins^{232} with direct comparisons to measured
properties from solid-state NMR experiments;^{137} it has also been used in studies of the insertion of
peptides into membranes^{233} and
peptide association and oligomerization in membrane environments.^{234} Studies of the mechanism by which
insertion of designed peptides into membrane bilayers proceeds, as
illustrated in Figure 5b, demonstrate
the utility of implicit models in the exploration of membrane-mediated
phenomena.

An EEF1-type model for implicit solvent and membrane studies
(IMM1)^{135} has been implemented
in CHARMM. Like EEF1,^{151} the method
utilizes Gaussian functions to describe the extent of burial of atoms in
different regions (i.e., the aqueous solvent versus the bilayer membrane).
IMM1 has been extended so as to account for the surface potential due to
anionic lipids,^{139} the transmembrane
potential,^{235} and the treatment
of membrane proteins with an aqueous pore.^{236} It has been used to obtain insights into the forces that
drive transmembrane helix association,^{180}^{,}^{237} calculate
pH-dependent absolute membrane binding free energies,^{238} and determine the voltage-dependent
energetics of alamethicin monomers.^{235}

#### Determination of Ionization States

Accurately simulating the electrostatic properties of a protein
depends upon the correct determination of the charged state of all ionizable
residues. The ionization state of a residue is determined by the free energy
difference between its protonated and unprotonated forms at a given pH. This
can be expressed in terms of the change in pK_{a}
(ΔpK_{a}) of the amino acid in a protein relative to
the intrinsic pK_{a} of the amino acid in solution. Correspondingly,
the free energy of transfer of the charged amino acid from the solvent to
the protein environment is equal to the reversible work required to ionize
the side chain in the protein minus the work needed to ionize it in an
isolated peptide in bulk water.^{239}
While the ΔpK_{a} can also be calculated using free
energy perturbation with explicit solvent molecules (see Section VI), a PB
or GB treatment representing the solvent as a dielectric continuum usually
offers a convenient and reasonably accurate approximation, because the
change in pK_{a} tends to be dominated by electrostatic
contributions to the solvation free energy. The calculation of
pK_{a} shifts can be done with the finite-difference PBEQ
module.^{191}^{,}^{192}^{,}^{240}
Estimates of the pK_{a} based on the PB equation can be improved by
introducing conformational sampling; e.g., calculated pK_{a} shifts
obtained by averaging over the coordinates from a molecular dynamics
simulation (see Section VIII) are usually more accurate than what is
calculated with a single structure.^{240}^{–}^{243} In
some cases, there is a strong coupling between the ionization states of the
residues and the predominant conformation of a protein. To address this
issue, a methodology has been implemented that combines the calculation of
pK_{a} with the generalized Born methods described above and
molecular dynamics. This approach, called pH-MD,^{127}^{,}^{129}
provides a means of coupling changes in protein and peptide conformations
with changes in the proton occupancy of titratable residues. The methodology
utilizes an extended Lagrangian to dynamically propagate the proton
occupancy variables, which evolve in the electrostatic field of the
protein/solvent environment through the GBMV^{216} or GBSW^{134} models.
The pH-MD method, which has been successfully applied to a number of protein
systems,^{127}^{–}^{129} extends the range of techniques
that are available for accurately representing electrostatic interactions in
solvated biological systems.

### E) Quantum Mechanical/Molecular Mechanical Methods

Because the quantum mechanical treatment of an entire biological
macromolecule requires very large amounts of computer time, combined quantum
mechanical and molecular mechanical (QM/MM) potentials are commonly used to
study chemical and biological processes involving bond cleavage and formation,
such as enzymatic reactions. In this approach, a small region (the
“QM region”) of the system, whose electronic structural
changes are of interest, is treated quantum mechanically and the remainder of
the system (“the MM region”) is represented by a
classical molecular mechanical force field. Typically, the former is a solute or
the active site of an enzyme, while the latter includes the parts of the protein
and the solvent environment that are not involved in the reaction. QM/MM methods
were first used for studying polyene electronic excitations in 1972 ^{244} and carbonium ion stabilization in the
active site of lysozyme in 1976.^{245}
Energy calculations based on the QM/MM methodology were carried out for
reactions in solution and in enzymes several years later. ^{246}

In the QM/MM approach, electrostatic effects as well as steric
contributions from the environment are incorporated directly into the electronic
structure calculations of the reactive region, affecting its charge polarization
and chemical reactivity.^{247} A QM/MM
potential employing semiempirical QM models (QUANTUM module) was first
implemented in CHARMM in 1987,^{248}^{,}^{249} through the incorporation of parts of
the MOPAC program.^{250} It was used for
the first molecular dynamics free energy simulation of an S_{N}2
reaction in aqueous solution;^{248}
numerous applications to enzymatic reactions have since been published (see, for
example, ^{251}^{–}^{256}). Because of its ability to treat
bond-forming and bond-breaking processes, to describe both the electronic ground
state and excited states,^{257} and to
reduce the required computer time dramatically relative to full quantum
mechanical calculations, the QM/MM approach has become the method of choice for
studying chemical reactions in condensed phases and in macromolecular systems
such as enzymes and ribozymes.^{258}^{,}^{259} In addition to the MOPAC-based QUANTUM
module and its derivative SQUANTM, the semiempirical, self-consistent charge
density functional tight-binding (SCC-DFTB) methods have been implemented
directly in CHARMM^{260}. Also, a number of
external electronic structure programs have been interfaced with CHARMM and its
molecular mechanics (MM) force fields for use in the QM part of QM/MM
calculations. In this subsection, the key features of the QM/MM module in CHARMM
are summarized. Details of the theory and applications can be found in Refs.
^{247}^{, }^{249}^{, }^{256}, and
^{261}.

#### Treatment of Boundary Atoms

In a combined QM/MM method, the most difficult part of the system to
model is the covalent boundary between the QM and MM regions;^{249}^{,}^{262} this problem is avoided if the boundary is between molecules
(e.g., between a “QM” ligand and an
“MM” solvated protein). For the general case, there
are three main criteria that the boundary between the QM and MM regions
should satisfy.^{263} First, the charge
polarization at the boundary should closely approximate that obtained from
QM calculations for the entire system. The effective electronegativity of a
boundary atom in the MM region should be the same as that of a real QM atom.
Second, the geometry at the boundary must be correct. Finally, the torsional
potential energy surface at the boundary should be consistent with the
surfaces arising from both QM and MM calculations.

Three approaches for treating the QM/MM boundary have been implemented in CHARMM. They are:

- Hydrogen link atom.
^{246}^{,}^{249}^{,}^{264}In this most commonly used approach, the valency of the QM fragment is saturated by a hydrogen atom that is introduced into the system along the covalent bond between the QM and MM regions. Although the link-atom approach has been used in numerous studies, it introduces additional degrees of freedom into the system; in addition, partial charges on the MM atoms that are closest to the link-atom must be removed to avoid convergence difficulties. The latter problem has been solved by the use of a double link-atom method^{265}that incorporates a balanced bond saturation of both the QM and MM fragments. - Delocalized Gaussian MM (DGMM) charges.
^{266}This method incorporates the delocalized character of charge densities on MM atoms using Gaussian functions, and it has been successfully combined with the double link atom approach. The method greatly simplifies the rules governing QM/MM electrostatic interactions. - Generalized Hybrid Orbital (GHO) method.
^{263}This method partitions the system at an sp^{3}atom. The boundary atom is included in both the QM calculation, with a fully optimized hybrid orbital and three auxiliary orbital’s, and also the MM force field, through the retention of the classical partial charge. The method is an extension of the frozen, localized orbital approach,^{267}and it neither introduces nor eliminates degrees of freedom. The GHO method has been implemented in CHARMM for semiempirical,^{263}SCC-DFTB,^{268}*ab initio*Hartree-Fock,^{269}and DFT^{270}quantum chemical models, the latter two through the GAMESS-US interface.

#### QM/MM Interactions

The interactions between the QM and MM regions are separated into an
electrostatic term, arising from the electric field of the MM atoms, and a
van der Waals component, accounting for dispersion interactions and Pauli
repulsions. Although the electrostatic interaction Hamiltonian employs
standard partial atomic charges of the force field, the van der Waals term
includes empirical parameters for the QM atoms. Thus, like DFT itself, the
QM/MM methods yield semiempirical potentials, which can be optimized by
comparing interaction energies obtained from QM/MM calculations to those
from fully quantum-mechanical optimizations for a database of biomolecular
complexes.^{249}^{,}^{271}^{–}^{276} The QM van der Waals parameters depend on the QM model and
the basis set; they have been the subject of extensive validation studies.
^{249}^{,}^{271}^{–}^{276}

The use of combined QM/MM potentials also provides the opportunity
to examine the contribution from specific energy components, including
electrostatic and polarization energies. A detailed analysis of the
polarization energies can be useful for developing empirical polarizable
force fields,^{271}^{,}^{277} as well as for studying the polarization
energy contributions to ligand-protein binding interactions.^{278} The energy decomposition method implemented
in CHARMM has been used to study inhibitor-protein complexes^{278} and the differential polarization energy
contribution to the reactant and transition state in enzyme reactions.^{279} Because the adequate treatment of
long-range electrostatic effects has a large influence on the accuracy of
combined QM/MM energies, an efficient linear-scaling Ewald method has been
implemented in QM/MM methods.^{280} In
addition, an approach using the generalized solvent boundary potential
method ^{29} (GSBP; see Section IV B)
for the treatment of electrostatics in QM/MM calculations is also available
in CHARMM.^{281}

#### Program Source for QM/MM Implementations

As mentioned, for the self-consistent-charge DFTB Hamiltonian
(SCC-DFTB) methods,^{282}^{,}^{283} and the MOPAC-derived
semiempirical methods (QUANTUM^{249}
and SQUANTM) (Nam, K., Walker, R. C., Crowley, M., York, D. M., Case, D. A.,
Brooks, C. L., III, Gao, J., in preparation.), the QM/MM potentials are
distributed as part of the CHARMM program. In 2005, an updated version of
the QUANTUM module, called SQUANTM, was developed. It features a more
efficient (i.e. faster) implementation of the QM/MM potential^{284} and is now the preferred module
for MOPAC-type QM/MM calculations in CHARMM. In addition, there is a CHARMM
interface to the MNDO97 program^{206};
see also Section III D. Interface routines have also been created for
*ab initio* molecular orbital and DFT packages, including
GAMESS-UK,^{266}^{,}^{285} GAMESS-US,^{286}^{,}^{287} CADPAC^{288} and Q-Chem.^{289} Interfaces to NWChem (5.0),^{290}^{,}^{291}
Gaussian (03),^{292} and MOLPRO
(2006.1)^{293} programs have been
implemented through the recently developed MSCALE functionality in CHARMM,
which is a general facility for combining potential energy functions and
models. The external QM programs to which CHARMM has been interfaced have to
be obtained from their authors. With the exception of Q-Chem, all of the
CHARMM/QM interfaces (either internal or external) are modular in form and
can be linked together with other functionalities in the CHARMM executable
to carry out energy minimization and molecular dynamics simulations. By
contrast, Q-Chem^{294}^{,}^{295} is interfaced to CHARMM through the exchange
of external files, so that CHARMM and Q-Chem are separate executables; this
facilitates the initial setup but slows down execution. Analytical first
derivatives have been implemented for all of the quantum chemical models. In
addition, numerical second derivatives can be calculated with the
*VIBRan* subcommand *DIAGonalize FINIte*.
Furthermore, numerical second derivatives for any of the CHARMM QM/MM
potentials can also be computed through the POLYRATE interface (see Section
VII F).

In all QM/MM calculations in CHARMM, each time an energy or force
evaluation is required, a self-consistent field (SCF) calculation is
performed. The electrostatic energy, which includes both QM and QM/MM
contributions, is added to the MM energy to yield the total energy for the
system. During a molecular dynamics simulation or energy minimization, the
density matrix from the previous step is used as the initial guess for the
next SCF calculation. In evaluating QM/MM interactions, the *ab
initio* molecular orbital and DFT methods include the
contribution from all MM partial charges of the system, i.e., without
cutoff, whereas the semiempirical modules have the option of using a cutoff
list as well as the particle mesh Ewald method for periodic systems.

### F) Restraining Potential Functions

In addition to the “physical” terms in the
potential energy function, a number of different restraint terms can be applied
to the system with CHARMM. These restraints are useful for the study of many
problems; they can be used to restrain the system to a given conformation during
various stages of a computation (e.g., energy minimization, equilibration), to
introduce a biasing potential for the performance of umbrella sampling in
potential of mean force calculations (see below)^{296}, or, more generally, to drive the system toward a known end
state in any kind of sampling procedure. The simplest type of restraint is the
spatial harmonic positional restraint, in which a selected set of atoms is
subjected to a quadratic potential relative to a given reference position in
Cartesian space. A harmonic restraint that is a function of the
“best-fit” root-mean-square deviation (RMSBFD) relative
to a reference structure can also be applied to selected atoms with arbitrary
weights. This restraint transiently reorients the structure relative to a
reference structure with a rigid best-fit coordinate transformation, based on
the selected atoms and weights, prior to the application of the distance
restraints. It is analytically differentiable.^{297} Internal coordinate and dihedral angle restraints can also be
applied. The Miscellaneous Mean-Field Potential (MMFP) module is a general
facility that is used to apply spherical, cylindrical, and planar restraining
potentials to a selected group of atoms or their center of mass. The module can
also be used to impose a distance restraint (on 2 sets of atoms), a pseudo-angle
restraint (3 sets) or a dihedral angle restraint (4 sets). Additionally,
restraints on the radius of gyration as well as on contact maps can be imposed
in CHARMM.^{298}^{–}^{300} Restraints can be applied that
correspond to user-specified molecular shapes (*SHAPe*) or
combinations of distances (*CONStrain DISTance*). For NMR-based
structural determination^{90}^{,}^{301}^{,}^{302} special-case distance restraints corresponding to the Nuclear
Overhauser Effect (NOE) can be imposed, as well as flat-bottomed dihedral
restraints based on dihedral angle data from scalar coupling constant
measurements.^{303} The NOE facility
also supports time-averaged distance restraints,^{304} which only require restraints to be satisfied on average. The
analytical forces introduced by all restraints in CHARMM are consistent with the
first derivative of the energy, which is particularly important for the best-fit
RMSD restraint.^{297}

## IV. Non-bonded interactions and Boundary Methods

To complete the description of the Hamiltonian for the system, the CHARMM
user needs to specify the option with which the non-bonded energy terms will be
computed. In molecular mechanics calculations all atoms, in principle, can interact
via the Lennard-Jones and electrostatic interaction terms with all other atoms.
However, the computational time for all-pair calculations scales as
*N ^{2}*, where

*N*is the number of atoms; this scaling behavior leads to an excessive computational cost for large systems. For all but the smallest systems, to save time, explicit calculation of the non-bonded pairwise interaction terms is usually limited to atom pairs whose interparticle separation is less than a user-specified cutoff distance; these pairs are stored in a list, which in many applications (such as molecular dynamics simulations) is not recalculated at every step. In CHARMM, this “non-bonded pair list” or “non-bonded list” may be atom- or group-based and is typically used in conjunction with various methods to treat the long-range interactions, such as extended electrostatics and long-range Lennard-Jones corrections, in addition to various truncation schemes. The non-bonded lists in CHARMM can be constructed using several types of algorithms based on spatial grids or clustering methods that speed up neighbor identification significantly for large systems.

The treatment of non-bonded interactions at and beyond the boundary of the
model system is also important in biomolecular calculations, because the part of the
system that is being modeled explicitly is often much smaller than the real system.
In a typical example, a single protein molecule surrounded by several thousand water
molecules in a 1000 nm^{3} volume is used to represent about 10^{12}
protein molecules and 10^{19} water molecules in a 1 μl volume
of a 1 μM protein solution. Early molecular dynamics simulations (e.g.,
the classic study of argon^{2}) showed that a
very small system (e.g., 256 Argon atoms) possessed many of the properties of the
macroscopic liquid. Nevertheless, the limited size of the simulated system can
introduce artifacts into the results. This can be due to the relatively small number
of particles that interact; i.e., the protein feels the influence of far fewer water
molecules in the model than it does in the real system. There are also possible
surface effects, since the small simulated system has a much larger surface
area/volume ratio than the real system; in the above example, this ratio is 10,000
times larger in the model system. The magnitude of such size-related effects can be
reduced by adding an energy term that mimics the properties of the neglected
surroundings, such as a solvent boundary potential (SBP), or by imposing periodic
boundary conditions (PBC) on the system. In PBC, all of the molecules in the central
cell are surrounded by other molecules, as if there were no explicit boundaries.
(Nonetheless, there can still be finite-size effects if the size of the central cell
is chosen to be smaller than some intrinsic correlation length of the molecular
system).^{305} Also, some studies have
indicated that spherical cutoff methods may introduce some artificial long-range
ordering of water at water/vapor and water/lipid interfaces, an effect that is
typically absent when lattice sum methods, which require PBC, are used for the
calculation of electrostatic interactions^{306}
(see Section IV. B).

The various methods in CHARMM for the treatment of boundaries and non-bonded
interactions are briefly described in this section. The reader is referred to the
CHARMM documentation for further details. The optimal methods to use in a given
problem are, as is often the case, a compromise between efficiency and accuracy. The
user may have to test the system using two or more of the available methods for
accuracy, via appropriate comparisons to experiment, and computational efficiency.
Currently, for MD simulations with the fixed-point-charge force fields, the best
(most accurate) approach is considered to be use of PBC systems with a non-bonded
cutoff of at least 12–14 Å, the force-shifting or
force-switching non-bonded options, the particle mesh Ewald treatment for long-range
electrostatics, and Lennard-Jones corrections for long-range van der Waals
interactions. However, if the system of interest is very large, or if extended
simulation times or many simulations are required, a less time-consuming Solvent
Boundary Potential (SBP) method may need to be employed. With the SBP methods, it is
desirable to include all non-bonded interactions, possibly via extended
electrostatics, or to perform electrostatic scaling,^{307} in addition to applying the appropriate reaction field method for
contributions beyond the boundary.

### A) Non-bonded Interactions

#### Spherical cut-off methods

Calculation of the non-bonded pairwise atomic interactions, i.e.,
interactions between atoms not directly bonded to one another, is typically
the most computationally demanding aspect of energy and energy-derivative
calculations. Since the number of possible pairwise interactions in a system
of *N* atoms grows as *N ^{2}*, the
explicit calculation of all Coulombic and LJ terms is usually impractical
for large systems. It is therefore necessary, in systems of greater than a
few thousand atoms, to truncate the non-bonded interactions at a
user-specified cutoff distance. The use of this approximation, which is
referred to as a spherical cutoff approach, means that only atom pairs
within the cutoff distance need to be included, greatly speeding up the
calculation. However, it may introduce artifacts. Most notably, a simple
truncation of the potential energy creates artificial forces at the cutoff
distance (because of the discontinuity in the energy), which can give rise
to artifacts in dynamics or structure.

^{308}Such artificial forces have been shown, for example, to significantly inhibit protein motion.

^{309}For this reason, proper truncation schemes for non-bonded interactions are an essential part of the spherical cutoff approach; this is especially true for the electrostatic interactions, which have a longer range than the van der Waals interactions. The simplest treatments consist of truncating the Coulomb interaction at the cutoff distance, while using a numerical procedure to decrease the unwanted influence of the truncation.

^{308}CHARMM provides a variety of truncation methods that act to smooth the transition in the energy and force at the cutoff distance, thereby reducing the errors in that region. These methods, which can be applied to both the electrostatic (Coulombic) and LJ interactions, include energy shifting and switching,

^{22}as well as force shifting and switching approaches.

^{308}

^{,}

^{310}The force shift/switch methods insure that, as the interatomic separation approaches the truncation distance, the forces go to zero in a smooth, continuous manner. These methods are, thus, particularly useful in MD simulations, where the forces determine the trajectories of the atoms, and they are the currently recommended approaches for most cases when a spherical cutoff is used. Molecular dynamics trajectories of even highly charged biomolecules like DNA have been shown to be stable if the appropriate smoothing functions and cutoff distances (usually at least 12 Å) are used (see below).

^{40}

^{,}

^{311}

#### Generating the non-bonded pair list

As stated above, the purpose of using finite cutoffs in energy
calculations is to reduce the number of non-bonded interaction terms.
However, the calculation to determine which atom pairs fall within the
cutoff distance can, itself, be time-consuming. Verlet first introduced the
idea of reducing the required frequency of this calculation by extending the
spherical cutoff region about each atom with an additional volume
shell,^{312} which is referred to
as a buffer region. In this technique, all of the atom pairs that are within
the outer cutoff distance are determined and stored in the non-bonded list,
while only the pairs that are within the inner cutoff are used in the energy
(and force) calculation. This approach reduces the computer time in two
ways: 1) for a fixed cutoff distance, the time for calculating energies and
forces from a non-bonded list grows linearly (rather than quadratically)
with the system size; and 2) in many calculations, the list does not have to
be recalculated at every step. In molecular dynamics or energy
minimizations, the atomic positions generally do not vary greatly from one
step to the next, so that the non-bonded list compiled with the buffer shell
contains all the atom pairs that will be required in the energy calculations
for the next several steps. The same list can, in principle, be used until a
pair of atoms in the system moves from beyond the outer cutoff to within the
inner cutoff; at the very least, one interparticle distance in the system
must have decreased by the width of the buffer shell before the list needs
to be recalculated. Accordingly, the “heuristic”
non-bonded option in CHARMM allows the list to be automatically updated
(recalculated) whenever one or more atoms have moved a distance greater than
half the width of the buffer shell. The user can alternatively specify a
fixed update frequency, typically from 10 to 50 steps/update; for cases in
which the system configuration is changing rapidly (e.g., protein folding
simulations), more frequent updates may be required. The larger the buffer
shell, the less frequently the non-bonded list needs to be recalculated (but
the longer it takes to calculate the list, itself). A typical buffer width
used in molecular dynamics simulations is 1–2 Å,
although for large systems and the slow listbuilder option (see below), it
is often advantageous to use a buffer width of 4 Å or more.

The use of a list and a buffer region does substantially reduce the
overall CPU time for many calculations, relative to the corresponding
non-list-based calculations. However, for large systems the fraction of time
that is spent compiling the non-bonded list can still be significant. This
is especially true if the list is calculated in a brute-force way, by
distance-testing all *N(N*−*1)/2*
atom pairs. The BYGROUPS algorithm in CHARMM speeds up list generation by
using standard CHARMM atomic groupings and compiling a group-group pair list
(which is much faster than compiling the atom-atom list), and then
calculating the atom-atom list from this shorter list. It is currently the
default listbuilder in CHARMM and supports nearly all the features and
options in the program (e.g., periodic boundary conditions and all free
energy methods). However, since the algorithm tests all possible group-group
pairs, it has *O(N ^{2})* time complexity and is slow
for large systems. Yip and Elber

^{313}developed a listbuilder algorithm that partitions the system into cubical spatial regions whose side length is equal to the outer non-bonded cutoff distance (which includes the buffer thickness) and then performs distance testing only between atoms in the same or directly adjacent cubes. This method, which was implemented in CHARMM as the BYCUBES method by Tom Ngo, has

*O(N)*(linear) time complexity and is faster than BYGROUPS for large systems. The “By-Cluster-In-Cubes” or BYCC algorithm

^{314}uses both the grouping and spatial partitioning techniques and, therefore, it has

*O(N)*time complexity and is faster than the other two algorithms. BYCC is approximately 2.2–2.8 times faster than BYCUBES across all system sizes and cutoff distances, and across a variety of platforms. The speed advantage of BYCC relative to BYGROUPS increases with system size and decreases with cutoff distance; for protein/water systems and a 12A cutoff distance, the relative speed advantage across various platforms is approximately 1 + 2×10

^{−4}

*N*(where

*N*is the number of atoms in the system). Hence for a 1000-atom system, the relative speed advantage is ~1.2, but for a 100,000-atom system it is ~20. For the latter system, MD simulations can be significantly faster using any of the cubical listbuilder algorithms (BYCC, BYCUBES, or BYCBIM), particularly for calculations using a thin buffer shell and high update frequencies. The memory requirements of BYCC are marginally higher than those of BYGROUPS and substantially lower than those of the other algorithms. In conjunction with the

*NBACtive*command, BYCC can also calculate the list for user-specified “active” parts of the system without the need for modifying the PSF. This partial-system list feature is fundamental to a general conformational search and structure prediction module that is currently being developed in CHARMM (the Z Module, ZEROM keyword). In addition, BYCC is the basis of the domain decomposition parallel scheme now being implemented in CHARMM (see Section X B). For a given set of atomic coordinates and cut-off distances, all three algorithms (BYCUBES, BYGROUPS, and BYCC) generate the same non-bonded list. All are also capable of generating a group-group pair list (as opposed to an atom-atom pair list), which is required by some CHARMM models (e.g., EEF1). In the group-based lists, a pair of groups are included if the separation between group centers is less than the cutoff distance. Such lists are sometimes used because they prevent the splitting of neutral groups into partially charged subgroups in the regions around the cutoff distance, which may lead to small errors in the electrostatic term. However, the use of a group list means that some atom pairs included in the energy calculations have interparticle separations greater than the cutoff distance. The BYCBIM algorithm extends the BYCUBES method to systems with images or periodic boundaries, and it (like BYGROUPS and BYCC) works for parallel simulations. It is currently the most efficient listbuilder in CHARMM for calculations involving image atoms.

#### Extended Electrostatics

The Extended Electrostatics model approximates the full
electrostatic interactions of a finite set of particles by partitioning the
electric potential and the resulting forces acting on a particle
*i* located at *r _{i}* into a
“near” and an “extended”
contribution.

^{315}The near contribution arises from the charged particles which are spatially close to

*r*(within a cutoff distance), while the extended contribution arises from the particles which are spatially distant from

_{i}*r*. The total electrostatic potential can be written as a sum of the two. Interactions between particles within the cutoff distance are calculated by a conventional pairwise additive scheme, whereas interactions between particles separated by a distance greater than the cutoff are evaluated using a time-saving multipole (dipole and quadrupole) approximation. The energy and forces are calculated by explicitly evaluating pairs in the near-neighbor list and using the stored potentials, fields, and gradients to approximate the distant pairs. The electric potential and its first and second derivatives are calculated only when the non-bonded list is updated and stored. This simple approximation is based on the assumption that, for distant pairs, the atomic displacements are sufficiently small between updates that the changes in their electrostatic interactions can be accurately calculated using local expansions. The approach is particularly useful for efficiently including electrostatic interactions at all distances in the treatment of a finite system, which is simulated using solvent boundary potentials such as SBOU,

_{i}^{27}SSBP,

^{28}and GSBP

^{29}(see Section IV.B). Examples are given in free energy difference calculations.

^{316}The method has been extended to include higher-order multipoles in a CHARMM implementation of the fast multipole method

^{317}(FMA module). An alternative method for the rapid calculation of the long-range electrostatic energies and forces in a system is Linear Time Complexity Reduction (LTCR). In this method the 1/

*r*dependence of the electrostatic term is approximated as a polynomial in the squared distance, so that the double sum over pairwise electrostatic interactions can be rewritten as a functional of single sums over single-particle terms.

_{ij}^{318}

#### Long-range Lennard-Jones corrections

Correction schemes for the LJ energy and virial beyond the atom
truncation distance have been implemented in CHARMM. One method (invoked
with the *LRC* option of the *NBONd* command)
determines the number density of each atom type in the system, and applies
an isotropic correction to the LJ energy and virial acting on each atom in
the system.^{8} A second method is
script-based, makes no isotropic assumptions, and calculates the correction
to the virial explicitly, resulting in a more accurate pressure and surface
tension. The latter method does not correct for the energy changes
associated with truncation^{319} and it
is significantly more costly than an LRC calculation; however, because the
virial correction does not need to be updated at every step in MD
simulations (instead, e.g., every 100 or 1000 steps), the overall cost of
the aniotropic correction can be reduced. Lastly, the long-range LJ
interactions can be calculated using the Isotropic Periodic Sum (IPS) method
described below. The IPS method calculates long-range interactions using the
so-called isotropic and periodic images of a local region around each
particle. It corrects not only energies, but also the forces and the virial.
Because IPS assumes that the distant environment around an atom is similar
to (and as heterogeneous as) the local environment, it preserves the density
of the system, and the incorporation of contributions from the long-range
interactions into the short-range potential gives more accurate results than
those obtained with an isotropic long-range correction.

### B) Boundary Conditions

#### Solvent Boundary Potentials

One approach for simulating a small part of a large system (e.g.,
the enzyme active site region of a large protein) uses a solvent boundary
potential (SBP). In SBP simulations, the macromolecular system is separated
into an inner and an outer region. In the outer region, part of the
macromolecule may be included explicitly in a fixed configuration, while the
solvent is represented implicitly as a continuous medium. In the inner
region, the solvent molecules and all or part of the macromolecule are
included explicitly and are allowed to move using molecular or stochastic
dynamics. The SBP aims to “mimic” the average
influence of the surroundings, which are not included explicitly in the
simulation.^{27}^{,}^{28} There are several implementations of the SBP
method in CHARMM. The earliest implementation, called the stochastic
boundary potential (SBOU), uses a soft nonpolar restraining potential to
help maintain a constant solvent density in the inner or
“simulation” region while the molecules in a shell
or buffer region are propagated using Langevin dynamics.^{27} By virtue of its simplicity, this treatment
remains attractive and it is sufficient for many applications.^{320}^{,}^{321} To improve the treatment of systems with irregular
boundaries in which part of the protein is in the outer region, a refinement
of the method has been developed that first scales the exposed charges to
account for solvent shielding and then corrects for the scaling by
post-processing.^{307}

The Spherical Solvent Boundary Potential (SSBP), which is part of
the Miscellaneous Mean Field Potential (MMFP) module (see Section III F), is
designed to simulate a molecular solute completely surrounded by an
isotropic bulk aqueous phase with a spherical boundary.^{28} In SSBP the radius of the spherical region is
allowed to fluctuate dynamically and the influence of long-range
electrostatic interactions is incorporated by including the dielectric
reaction field response of the solvent.^{28}^{,}^{29} This approach has
been used to study several systems.^{322}^{–}^{325}
Because SSBP incorporates the long-range electrostatic reaction field
contribution, the method is particularly useful in free energy calculations
that involve introducing charges.^{322}^{–}^{325}

Like the SBOU charge-scaling method,^{307} the Generalized Solvent Boundary Potential (GSBP) is
designed for irregular boundaries when part of the protein is outside the
simulation region.^{29} However, unlike
SBOU, GSBP includes long-range electrostatic effects and reaction fields. In
the GSBP approach, the influence of the outer region is represented in terms
of a solvent-shielded static field and a reaction field expressed in terms
of a basis set expansion of the charge density in the inner region, with the
basis set coefficients corresponding to generalized electrostatic
multipoles.^{29}^{,}^{326} The solvent-shielded static field from the
outer macromolecular atoms and the reaction field matrix representing the
coupling between the generalized multipoles are both invariant with respect
to the configuration of the explicit atoms in the inner region. They are
calculated only once (with the assumption that the size and shape of inner
region does not change during the simulation) using the finite-difference
Poisson-Boltzmann (PB) equation of the PBEQ module. This formulation is an
accurate and computationally efficient hybrid MD/continuum method for
simulating a small region of a large macromolecular system,^{326} and is also used in QM/MM approaches.^{281}^{,}^{327}

#### Periodic Boundary Conditions and Lattice Sum Methods

CHARMM has a general image support facility that allows the
simulation of symmetric or periodic boundary systems. All crystal forms are
supported, as well as planar, linear, and finite point groups (such as
dimers, tetramers, etc.). Figure 6
depicts the simulation of a virus capsid where icosahedral symmetry has been
imposed so that it is necessary to represent explicitly only
1/60^{th} of the entire capsid.^{328} It is also possible to build a unit cell related to its
neighbors with any space group symmetry, to optimize its lattice parameters
and molecular coordinates, and to carry out a vibrational phonon analysis
using the crystal module (CRYSTAL),^{329} which is an extension of the original image facility.^{22}^{,}^{330} Simulations allowing lipids in opposing membrane leaflets to
exchange can be carried out using *P2 _{1}* boundary
conditions.

^{330}The image facility achieves its generality by treating image atoms (coordinates and forces) explicitly, thus avoiding the size and transformation limitations inherent in the more commonly used minimum-image convention. This also allows the virial to be computed with a single-sum method for a rapid evaluation of the pressure.

^{8}Bond linkages (with additional energy terms including bond angle, dihedral angle, and improper dihedral angle terms) can be introduced between the primary atoms and image atoms in order to allow the simulation of “infinite” polymers, such as DNA, without end effects. For infinite systems, the simulation can be restricted to the asymmetric unit because arbitrary rotations, translations, and reflections can be applied to generate the coordinates for larger versions of the system (see also Figure 6). To ensure better numerical stability in the volume and shape fluctuations of the unit cell during constant-pressure Nosé-Hoover-Andersen-Klein

^{331}dynamics, the symmetry operations on the central cell are handled internally by keeping the atomic coordinates in a symmetric projection of the unit cell vectors. The latter condition is imposed to prevent unwanted torque on the system due to box shape changes (e.g., in the triclinic case).

**...**

If periodic boundary conditions are imposed on the system, the electrostatic energy can be expressed as a lattice sum over all pair interactions and over all lattice vectors. Namely,

where *r _{i}* is the position vector
and

*q*is the charge of particle

_{i}*i*,

*N*is the number of atoms in the unit cell,

*$\stackrel{\u20d7}{m}$*is the lattice vector of the (real space) periodic array of unit cells, and the prime on the sum indicates that

*j*≠

*i*when

*$\stackrel{\u20d7}{m}$*= 0. This sum converges conditionally—i.e., it depends on the order of the summation over unit cells—and slowly.

The method developed by Ewald^{332} transforms the summation to two more complicated but
absolutely and rapidly convergent sums, plus a
‘self-energy’ term and a
‘dipole’ term. The dipole term, which captures the
conditional convergence of the original sum and includes the external
reaction field conditions, can be made to vanish (see below). The total
electrostatic energy,
*U*(*$\stackrel{\u20d7}{r}$ ^{N}*), then
equals

where *erfc* is the complementary error
function, κ is a constant, *k* is the reciprocal
space lattice vector, *V* is the volume of the unit cell, and
*I* is the imaginary unit. The first term is a reciprocal
space sum over all pairwise interactions (both short and long-range) in the
infinite lattice, in which the charge distributions about each particle are
spherical Gaussians. The second term is a direct sum over all short-range
pairs and consists of two components: A) the point-charge interactions
between the short-range pairs and B) a term that cancels the contributions
of these pairs in the first term (reciprocal space sum); i.e., the latter
component subtracts the interactions between the Gaussian charge
distributions for all short-range pairs. The third term, which is the
self-energy term, provides the same type of cancellation for each Gaussian
charge distribution in the unit cell interacting with itself. The parameter
κ does not affect the total energy and forces, but rather
adjusts the relative rates of convergence of the real and reciprocal space
sums; it is usually chosen so as to optimize the balance of accuracy and
efficiency of the calculations. If κ is chosen to be large
enough, only the *$\stackrel{\u20d7}{m}$* = 0 elements
contribute to the second (short-range) term, and it reduces to the
minimum-image convention sum. The triple sum of the first term can be
rewritten as a double sum over *$\stackrel{\u20d7}{k}$* and i. The
dipole term^{333}^{,}^{334} can be added to account for the effects of
the total dipole moment of the unit cell, the shape of the macroscopic
lattice, and the dielectric constant of the surrounding medium. However,
this term vanishes in the limit that the net dipole moment of the unit cell,
which is origin-dependent and affected discontinuously by image wrapping,
vanishes, or the external dielectric constant goes to infinity (so-called
“tin foil” boundary conditions). In CHARMM, because
interactions between 1,2 and 1,3 bonded atom pairs are excluded from the
point-charge part of the direct sum and, hence, do not appear in the second
term of Eq. (8), their
contributions to the reciprocal sum are corrected for in a separate
calculation (EWEX term).

Recent variants of the Ewald method, which employ pairwise cutoff
lists for the direct sum, charges on grids, and fast Fourier transforms,
greatly enhance computational performance. One of these, the particle mesh
Ewald (PME) method,^{335}^{,}^{336} has been incorporated into CHARMM.
Although convergence of the Ewald summation requires neutrality of the unit
cell, the Ewald and PME methods can be used for a system carrying a net
charge by the effective superposition of a structureless neutralizing
background onto the unit cell. CHARMM optionally computes both the energy
and the virial correction terms for the net charge case,^{337} which may be included with a user-specified
scale factor that is optimally determined by the dielectric.^{338}^{,}^{339}
The treatment of the long-range electrostatics based on PME, and the
constant-pressure and constant-surface-tension simulation algorithms^{340} are implemented for the crystal
symmetries as defined in the CRYSTAL facility. Consequently, the CRYSTAL
facility must be used for such calculations in CHARMM.

While the Ewald and PME methods are formally applicable to periodic
systems, it is also possible to use them to calculate the electrostatic
energy and forces within a finite isolated cluster without cutoff effects.
The method relies on truncation of the 1*/r* Coulomb
potential at a finite range *R*. To remove all interactions
between charges belonging to neighboring unit cells while keeping those
within a finite cluster of diameter *s*, it is sufficient to
sum over all lattice vectors using a filter function-modified Coulomb
potential^{341} with finite range
*R,* such that
*s*<*R*<*L-s,*
where *L* is the center-to-center distance between
neighboring cells. With this modification, the PME methods can be used to
rapidly compute energies and forces with no interference from periodicity
and with nearly linear scaling.^{342}
PBC with the minimum-image convention can also be used in CHARMM through the
PBOUND module, but the facility does not currently support constant-pressure
MD and an Ewald description of the electrostatics.

#### Isotropic Periodic Sum (IPS) for long-range interactions

The IPS method^{343} is a
general method for calculating long-range interactions, that, unlike
Ewald-based methods, does not sum contributions over lattice images.
Instead, so-called “isotropic” periodic images are
assumed to represent remote structures. The isotropic and periodic character
of the images simplifies the summation of long-range interactions relative
to a summation over lattice images. The IPS method reduces the calculation
of particle interactions to the calculation of short-range interactions
within a defined region (a cutoff distance) plus long-range interactions
given by isotropic periodic sums. Due to the periodicity of the image
regions, the total forces acting on one atom from a second atom and all of
its images goes smoothly to zero at the boundary of the local region about
the first atom, so that no truncation is needed. Simulation results have
shown that for a Lennard-Jones fluid, the energy, density, and transport
coefficients are nearly independent of the cutoff distance for all but the
shortest cutoff distances (less than ~ 8A).^{343}^{,}^{344}

Analytic solutions of IPS have been derived for electrostatic and
Lennard-Jones potentials, but it can be applied to potentials of any
functional form, and to fully and partially homogeneous systems, as well as
to non-periodic systems. Customized formulations of the method have been
developed for use in systems with 1- or 2-dimensional homogeneity (1D or 2D
IPS); for example, 2D IPS can be used for membrane systems. For liquid/vapor
interfaces, 2D IPS is exact when the interface is homogeneous in the
interfacial plane. Because 2D IPS assumes a finite thickness of an
interfacial system, it is not suitable for liquid-liquid interfacial systems
where the thickness is infinite. For liquid/liquid interfaces, such as lipid
bilayers in water, PME/IPS (PME for electrostatics and 3D IPS for van der
Waals interactions) appears to provide the most realistic conditions. The
PME/IPS method is in excellent agreement with large cutoffs for interfacial
densities and dipole potentials and only slightly underpredicts the surface
tensions^{345}, though the method
is not exact for the long-range interactions in these inhomogeneous systems.
For true lattice systems where long-range structure can be accurately
described by periodic boundary conditions, IPS is less accurate than lattice
sum-based methods like PME. Recent advances in the IPS method to include a
second longer cutoff (Wu, X. and Brooks, B.R., submitted for publication)
have eliminated many of the aforementioned problems.

The IPS method is computationally efficient and is readily parallelized, in part because, unlike PME, it does not require the calculation of Fourier transforms. The communication scheme is similar to that for other cutoff-based methods.

## V. Minimization, Dynamics, Normal Modes, and Monte Carlo Methods

An essential element of CHARMM functionality is the calculation of the energy and its derivatives, because this makes possible the study of many properties by energy minimization, Monte Carlo sampling, normal mode analysis, and molecular dynamics. CHARMM provides a number of minimization methods and several approaches to the propagation of trajectories that allow for the sampling of a variety of ensembles.

### A) Energy Minimization

CHARMM supports a number of minimization methods
(*MINImize* command) that rely on either the first
derivatives or the first and second derivatives of the energy function (Eq. 1). Multiple methods are
included in the program because each one has its advantages. They include the
simplest method, Steepest Descent (SD), and other first-derivative methods such
as a variant of the Fletcher-Powell algorithm and a conjugate gradient technique
(CONJ). The latter two methods obtain better convergence than SD by including
information on the derivatives from prior points of the minimization. The
second-derivative methods operate in either the full space of the Hessian
(Newton-Raphson, NRAP) or in a subspace of the full Hessian (Adopted-Basis set
Newton-Raphson, ABNR). The NRAP algorithm has additional features that can force
it off a saddle point; these are useful, for example, when the initial structure
has unwanted symmetry. A minimization method that is intermediate between the
first-derivative and full Hessian methods, the truncated-Newton (TN) minimizer
(TNPACK), has also been implemented in CHARMM.^{346} This approach is comparable to ABNR with respect to
computational efficiency, though its convergence is better, particularly for
systems with less than 400 atoms. In general, the first-derivative methods are
more robust in the initial stages of energy minimization calculations, whereas
the NRAP and ABNR or TNPACK techniques provide better convergence to the local
minimum when there are no large gradient components. Typically, initial
minimizations are performed using the first derivative methods, usually
beginning with SD, especially in cases where there are bad contacts causing a
large initial gradient. This is followed by the NRAP method for small systems
(< 300 atoms), or ABNR or TNPACK when NRAP matrices become too large.
Methods such as SD and CONJ are also more robust than second derivative methods
when faced with energy and force discontinuities that occur with some energy
terms and options (e.g., electrostatic truncation).

In addition to potential energy minimization, local saddle points may be
identified in CHARMM by minimizing the norm of the potential energy gradient
(*GRAD* option of *MINImize* command).
Depending on the initial conditions, the search will either be terminated at a
minimum or a saddle point on the potential energy surface. This feature is
primarily used for determining first-order saddle points. Since the
second-derivative matrix is employed to calculate first derivatives of the
target function in this method, it is much slower than ABNR and NRAP and,
therefore, is not recommended for more standard energy minimizations.
Alternatively, saddle points can be located using the *SADDle*
option associated with NRAP. This option identifies the most negative
eigenvalue(s) and maximizes along the corresponding eigenvector(s) while
minimizing in all other directions. Another approach to finding accurate saddle
points is implemented as part of the TREK module (Section VII A).

### B) Molecular Dynamics

Classical molecular dynamics (MD) simulations are used for evaluating
the structural, thermodynamic and dynamic properties of biomolecular
systems.^{4} Such simulations require
integration of Newton’s equations of motion, which determine the
coordinates of the system as a function of time. The principal assumption in the
use of MD is that classical dynamics is adequate and that quantum corrections to
the atomic dynamics are negligible. This assumption is valid for most problems
of interest in macromolecular biological systems; i.e., above ~ 50 K, for a
given biomolecular potential energy surface, the classical and quantum
mechanical descriptions of the dynamical properties of interest effectively
coincide.^{347}
^{24}^{,}^{348} Notable exceptions arise in chemical reactions (proton
tunneling; see Section III E). Also, for the estimation of the absolute entropy
(and free energy), higher temperatures are required to reach the classical
limit; however, for entropy and free energy difference calculations the
classical treatment often provides a good approximation even at room temperature
because the low-frequency modes make the dominant contribution.^{349} This, of course, provides the theoretical basis
for the widely used classical free energy simulation methods (Section VI A).

Molecular dynamics trajectories in CHARMM are controlled by the general
and multi-optioned *DYNAmics* command. A single call to
*DYNAmics* can initiate, propagate, and terminate a
trajectory, as well as specify options for the dynamics integration scheme,
non-bonded interactions, the image atom list, thermostats, heating schedules,
initial assignment and rescaling of velocities, statistical ensembles, system
recentering, the generation of binary trajectory and velocity files, the output
of formatted files containing coordinates, forces, and velocities, the writing
of energy statistics to standard output, and the reading and writing of restart
files. The algorithms by which the atomic positions of the system are propagated
after the computation of the forces are called dynamics integrators. There are
currently five supported integrators within CHARMM: ORIG, LEAP, VVER, VER4, and
VV2. Each integrator is unique and has its own strengths and limitations. The
standard integrator, LEAP, is based on the Verlet leap-frog algorithm. It is the
most general and most widely used of CHARMM’s integrators and has
the largest number of supported features. The leap-frog algorithm was selected
to be the standard because, in its simplest form, it is an efficient,
high-precision integrator with the fewest numerical operations.^{8} The newest integrator, VV2, which is based on a
velocity Verlet scheme with improved temperature and pressure control,^{350} has been implemented to support
polarizable models based on the classical Drude oscillators.^{104} The oldest integrator, ORIG,^{22} is based on the lower-precision Verlet 3-step
method. This is the most limited of the CHARMM integrators, but it is retained
for historical reasons and testing of other integrators. The original velocity
Verlet integrator, VVER, is also a high-precision integrator that supports a
multiple-time-step method (MTS),^{351} but
it is otherwise limited (e.g., no pressure calculation). The leap-frog
integrator has been extended to a theoretical 4^{th} spatial dimension
in the development of the VER4 integrator^{352} for the purpose of enhanced conformational sampling in
4-dimensional molecular dynamics (Section VI E); the integrator is usable only
for this function.

The standard Verlet MD integration scheme or one of its variants is
often used to perform simulations in the micro-canonical ensemble
(*NVE*), in which the total energy and volume are constant.
The *NVE, NVT* (canonical) and *NPT*
(isothermal-isobaric) ensembles are the “workhorses” of
contemporary molecular dynamics simulations. *NPT* is often
useful during equilibration for achieving the desired water density in a system
with explicit solvent; once the system is stable, a change to the
*NVE* or *NVT* ensemble may be appropriate.
For testing and evaluating new simulation methods, the *NVE*
ensemble has the advantage that energy conservation can be used as a necessary
(though not sufficient) diagnostic for the validity of the calculations. The
leap-frog integrator also calculates a high-frequency corrected total
energy^{353} which eliminates the
time-step dependence of the total energy. Since the Verlet integration methods
are symplectic, in the absence of constraints like SHAKE,^{354} this corresponds to monitoring energy drift with
a shadow Hamiltonian.^{355} Moreover,
constrained dynamics with Verlet and SHAKE is symplectic if the constraints are
introduced with sufficient accuracy.^{356}

Using this approach, the fluctuation in the total energy has been typically observed to decrease by one order of magnitude or more. By eliminating high frequency noise, small changes in the total energy become more readily observable. A similar approach is also used for the piston degrees of freedom (see below) to allow an accurate estimate of the transfer of heat into a constant temperature and pressure system. Both velocity reassignment and velocity scaling can be performed with the Verlet-type integrators to couple atoms in the simulation volume to a heatbath; velocity scaling is often used to gradually heat or cool a system targeting a desired temperature.

All the integrators are consistent with the use of SHAKE-type
methods^{354} for the imposition of
holonomic constraints. These constraints can be employed, for example, to fix
the length of covalent bonds involving hydrogen atoms when these motions are not
of specific interest, as is the case in most applications of MD simulations not
involving vibrational spectrum analysis or proton NMR. SHAKE-type constraints
are used for fixing the relative positions of charges that are not localized on
atoms, as in the early ST2 water model,^{73}
the TIP4P model,^{50} and other more
elaborate water models. Eight types of holonomic constraints are available in
CHARMM. When more than one type of constraint is applied, an iterative,
self-consistent approach is used to satisfy all constraints. The supported
constraints include: *SHAKe* (simple distance constraints),
*LONEpair* (general massless particle constraint facility;
preprocessor keyword LONEPAIR), *CONStrain FIX* (atomic
positional constraints), *ST2* (required restraints for the ST2
water model that are activated on PSF *GENEration* when ST2 is
the residue type), *FIX* (a *TSM* subcommand used
for fixing internal coordinates), *RIGId* (a
*SHAPes* option that creates a rigid body object), SHAKA4 (a
*SHAKe* subcommand of *FOUR* for constraints
in the 4th spatial dimension), and *PATH* (path constraints to
keep the structures on a particular hyperplane, used with the RXNCOR facility;
see Section VII C). *SHAKe* allows the use of a longer timestep,
typically 2 fs, when integrating Newton’s equations of motion.^{351}^{,}^{354}^{,}^{357} The lonepair
facility is a general constraint code for all “massless”
particles in CHARMM, with the exception of those in the ST2 water model. On each
iteration, massless particle positions are determined relative to atomic
positions, and the forces calculated on massless particles are transferred to
atoms in such a manner as to preserve the net torque and force. The use of the
*CONStrain FIX* command can significantly improve speed,
since it results in the removal of constrained atomic pairs or groups from the
non-bonded lists required for the calculation of the energy and forces. All of
these constraints include a pressure correction term, which arises from the
fictitious forces on the system that maintain the constraints.

#### Ensembles for Dynamics

Several constant temperature (*NVT*, canonical
ensemble) and pressure (*NPT*) methods can be used with the
equations-of-motion integrators. Constant temperature and pressure
simulations can be performed with CHARMM using methods that are based on the
ideas of extended Lagrangian dynamics.^{331}^{,}^{358} This approach
ensures that well-defined statistical ensembles are achieved. Also,
multi-temperature controls are available, through which the temperatures of
different parts of the system are coupled to different thermostats. This can
aid in equilibrating the system or in keeping the system at the desired
temperature when its components (e.g., protein and its water environment)
have significantly different properties; an interesting application of such
multiple thermostats involved keeping a protein and its solvent shell at
different temperatures.^{359} The
Nosé-Hoover heat bath methods work with the leap-frog Verlet and
velocity-Verlet integrators in CHARMM. For *NPT* simulations,
the Hoover heat bath method can be used in conjunction with a pressure
coupling algorithm designated as the Langevin Piston.^{360} This is a robust method in which
Langevin-type random and frictional forces are applied to piston degrees of
freedom (e.g., during MD equilibration) to obtain a valid thermodynamic
ensemble. Methods for other ensembles as variants of this approach are
available in CHARMM, as described in the work by Zhang *et
al*.^{340} A corresponding
method is used in simulations of lipid bilayers and other interfacial
systems in which a constant surface tension is maintained.

A modified velocity-Verlet algorithm is available to simulate
systems in which induced polarizability is represented with classical Drude
oscillators that are treated as auxiliary dynamical degrees of freedom.^{104} The familiar self-consistent field
(SCF) regime is simulated if the auxiliary Drude particles are reset to
their local energy-minimum positions after every timestep of the physical
atoms, but this procedure is computationally inefficient. The SCF regime can
be approximated efficiently with two separate Nosé-Hoover
thermostats acting on the polarizable atoms and their auxiliary Drude
particles. The first thermostat, coupled to the center-of-mass of the
atom-Drude pair, keeps the true physical degrees of freedom at any desired
temperature. The second low-temperature thermostat (~1 K), acts on the
relative atom-Drude motion within the reference frame of the center-of-mass
of each pair to control the amplitude of the classical oscillators relative
to their local energy minima. In its CHARMM implementation, the
double-thermostat velocity-Verlet algorithm allows efficient SCF-like
constant-pressure constant-temperature molecular dynamics simulations of
systems of polarizable molecules with a timestep of ~1 fs.

In addition, a modified Berendsen method^{361} has been implemented that allows for both
constant temperature and constant pressure simulations. While the Berendsen
approach works well for small systems and for very weak coupling constants,
and has been widely used, it may lead to differential heating of
heterogeneous systems, most notably interfacial systems.^{360} Furthermore, the resulting MD trajectory does
not correspond to any thermodynamic ensemble. Thus, the methods for
*NVT* and *NPT* simulations described
above are recommended over this method, despite its advantage of ease of
use.

#### Non-Verlet Integrators

Langevin dynamics (LD) simulations, which propagate the system
coordinates with the Langevin equation,^{362} rather than Newton’s equations, include random
and frictional forces that mimic the effects of the environment on the
dynamics of the simulated system.^{363}^{,}^{364} Coupling a fully
solvated system to a Langevin heatbath is an effective way of maintaining a
constant-temperature ensemble. This type of Langevin heatbath coupling can
be used as a complement to the implicit solvation methods (Section III D),
which treat the effect of solvent on the solute energy but do not include
the frictional and dissipative properties of solvent. LD is also used in
stochastic boundary simulations. It is suitable for studying long-time-scale
events that occur in macromolecules, such as protein folding. LD is also
useful for small systems, such as small molecules in the gas phase, where
the temperature based on the atomic velocities is poorly defined and the
free energy transfer between modes can be very slow.

### C) Normal Mode Methods and Harmonic Dynamics

CHARMM has a comprehensive utility for molecular vibrational analysis,
called VIBRAN. The VIBRAN module includes basic tools for calculating normal
modes of vibration, either with the full atomic basis or with a reduced basis in
which some degrees of freedom are constrained. An example of the latter is the
calculation of normal modes using only the dihedral angle degrees of freedom.
The module also has the capacity to generate quasi-harmonic modes of vibration
from MD simulations with either the full or reduced basis. Quasi-harmonic modes
of vibration are the normal modes of vibration of a harmonic potential energy
surface that would generate the same fluctuation matrix, when every mode is
populated with *k _{B}T*

^{#}of energy, as that calculated from a molecular dynamics simulation. There is also an extensive set of analysis tools that facilitate the analysis of normal modes. The VIBRAN facility was summarized in the original CHARMM paper

^{22}and later described in considerable detail.

^{365}

^{–}

^{367}This section will primarily focus on developments that have occurred since the latter publications.

The VIBRAN module provides the means for calculating thermodynamic
properties of a system from the vibrational analysis in terms of normal or
quasiharmonic modes. An example is the calculation of the configurational
entropy from normal modes obtained via quasi-harmonic analysis. These results
can be combined with the overall rotational and translational contribution to
the entropy and with other energetic information (i.e., vibrational enthalpies,
free energies of solvation from continuum electrostatic methods) to obtain the
free energies of ligand-protein,^{368}
protein-protein^{369} or protein-DNA
interactions.^{141}

There has been considerable effort in developing efficient methods for
the harmonic analysis of very large biomolecules when only a few
lowest-frequency modes are of interest. A number of studies^{370}^{–}^{374} have shown that low-frequency modes, which reflect the natural
flexibility of the system, often provide important functional information about
biomolecules that undergo significant conformational transitions. One approach
involves an iterative diagonalization in a mixed basis (DIMB),^{375}^{,}^{376} which
requires considerably less computer memory than the full basis calculation, yet
converges to the same result. The method involves repetitive reduced-basis
diagonalizations, where the reduced bases are constructed partially from the
approximate eigenvectors and from the Cartesian coordinates. Another approach
breaks the system into rigid blocks, typically one residue each, or larger. Due
to their collective nature, the low-frequency modes of the system can be
computed rather accurately with such a block normal mode (BNM;
rotational-translation-block) approach.^{377}^{,}^{378} In this approach,
the atomic Hessian is projected into a subspace spanned by the rotational and
translational motions of the blocks. The projection dramatically reduces the
size of the matrix to be diagonalized and thus the cost of computation. The
current implementation in CHARMM also has the option of using an iterative
diagonalization procedure for sparse matrices, which makes it possible to obtain
low-frequency modes of large biomolecular assemblies such as the 30S and 50S
ribosome.^{379} Compared to even more
simplified approaches such as the elastic network model^{380}^{,}^{381}
(which is also available in CHARMM; see Section VII E), the BNM method has the
advantage of using the full physical potential energy function (Eq. 1), which makes it possible to obtain
detailed information for many kinds of biomolecules^{382}^{,}^{383} and
permits the inclusion of co-factors and ligands in a straightforward way. A
comparison of CHARMM BNM^{383} with a
series of elastic models demonstrated the superiority of the former for
calculating anisotropic B factors.

Normal mode calculations can also be carried out with QM/MM potential
functions.^{384} This capability is
especially useful for spectroscopic characterization of the active sites of
metalloenzymes,^{379} characterization
of stationary points along reaction pathways in enzymes, and estimates of the
vibrational contributions to the activation free energy for reactions in complex
systems.^{254}^{,}^{385} With careful parameterization, QM/MM vibrational
analysis can also be used to compute non-linear infrared spectra,^{386} which contain valuable information
regarding the fast time-scale dynamics of condensed-phase systems. The standard
implementation in the release versions of CHARMM (see Section XI) computes the
second derivative matrix using finite differences of the analytical first
derivatives for many of the QM methods, including AM1, PM3, and SCC-DFTB, which
are included in CHARMM (Section III E), and other *ab initio* or
density functional methods that are available in separate QM packages. QM/MM
analytic second derivative support has been implemented for the Q-Chem/CHARMM
interface.^{387} Also, analytical
computation of QM/MM second derivatives^{384} are currently available in a specialized version of CHARMM in
conjunction with the GAMESS-US package.^{286}^{,}^{287}

#### Quasi-harmonic analysis

Quasi-harmonic normal modes can be extracted from a trajectory by
diagonalizing the mass-weighted covariance matrix of the atomic
displacements from their average positions.^{365} These modes are similar to the normal modes obtained from
diagonalization of the Hessian, but contain anharmonic contributions as
well. Once the covariance matrix has been obtained, the diagonalization can
be performed on the submatrix corresponding to any subset of atoms,
effectively allowing the analysis to be applied to individual residues, or
just to the backbone or side chains. The modes, harmonic or quasi-harmonic,
can be saved to disk for visualization, or their character can be further
analyzed in terms of the contributions of individual atoms. The eigenvalues,
which are related to the frequencies of the motions, can be inserted into
the 3*n*-dimensional harmonic oscillator expressions for the
entropy, enthalpy or heat capacity^{388} of the (sub)system, where *n* is the number
of atoms. The calculation of converged quasi-harmonic entropies often
requires lengthy trajectories.^{389} In
addition to the configurational (vibrational) entropy, the rigid-body
translational/rotational contribution to the entropy can also be computed
from a trajectory. For this, the (quasi)harmonic interpretation is not
required and, in the absence of mass weighting, the method is identical to
the standard multivariate statistical method of principal component analysis
(PCA),^{390} with the computed
frequencies inversely proportional to the variances of the atomic
displacements of the trajectory along the eigenvectors. PCA has been used to
extract dominant motions in proteins in, for example, “essential
dynamics”.^{391}

### D) Monte-Carlo Methods

In Monte Carlo (MC) simulations, random changes (moves) made to the
configuration of a system are accepted or rejected in such a way as to obtain a
chain of states that samples a well-defined probability distribution.^{392} MC need not follow a realistic path
for ensemble averages to converge, which makes it useful for simulating
relaxation processes that occur on timescales that are much longer than the
fastest motions of the system (typically bond stretches in biomolecular
systems). Despite this advantage, there are far fewer MC than MD studies to date
because initial comparisons between the two methods suggested that MC samples
protein configurations inefficiently.^{393}
However, improved move sets now allow much faster decorrelation of observables,
making MC the method of choice in many cases requiring the search of a large
conformational space.^{394}^{,}^{395} Certain features and applications of
the MC module in CHARMM are summarized here; for more details, see Hu *et
al*.^{395}

#### Background

The sampling of a system with a series of (pseudo)randomly
generated states is a Monte Carlo process. From these states, an estimate of
the thermal average of quantity *B* over all states
*x*_{i} in a system at temperature
*T* is given by

where *n* is the number of sampled states,
*E*(*x _{i}*) is the energy of

*x*i and

*P*(

*x*) is the probability of

_{i}*x*

_{i}appearing in the sampled population. Metropolis et al. (1953)

^{392}first noted that an efficient choice of

*P*(

*x*) is the Boltzmann probability itself— i.e., (

_{i}*P*(

*x*) exp(−

_{i}*E*(

*x*)/

_{i}*k*)). In this case, Eq. (9) reduces to a simple arithmetic average: $\langle B\rangle \approx {\sum}_{i=1}^{n}B({x}_{i})/n$. One of the aims of Monte Carlo calculations is to sample the system according to the canonical probability distribution; many other importance sampling methods are based on a similar approach. In the Metropolis method, this weighting of sampled states can be achieved by accepting or rejecting a series of changes from a predefined set of possible ones (a move set) according to the acceptance probability

_{B}T*P*

_{acc}_{,}

*≈ min(1,exp(−Δ*

_{i}*E*/

_{i}*k*)), where Δ

_{B}T*E*is the change in energy between the

_{i}*i*th state (conformation) and the previously accepted one. The series of accepted states so generated is referred to as a Markov chain. The Metropolis method satisfies the condition of detailed balance, which implies that, at equilibrium, the average number of moves between two arbitrary states is the same in either direction; this is sufficient (though not necessary) for sampling in the canonical ensemble.

#### Ensembles

MC in CHARMM can sample from the canonical
(*NVT*),^{392}
isothermal-isobaric (*NPT*),^{396} and grand canonical (μ*VT*)^{397} ensembles. Because the grand
canonical MC algorithm allows particles to be inserted into and deleted from
the system as though exchanging with a bulk solvent reservoir of known
excess chemical potential (μ), it is very useful for solvating
macromolecules, especially ones with restricted access to cavities.^{398} Woo *et al*.^{397} describe the grand canonical MC
implementation in CHARMM, which includes cavity-bias^{399} and grid-based^{400} algorithms for selecting the sites of
insertion; Hu *et al*.^{401} calibrate the method to determine the value of μ
required to reproduce bulk water densities with the TIP3 model^{48}^{,}^{50} and standard non-bonded cutoffs in a periodic system.

In addition to the physically meaningful ensembles described above,
MC in CHARMM can sample with a number of additional weighting schemes. These
include the Tsallis or “generalized” ensemble^{402}^{,}^{403} and the multicanonical or constant-entropy ensemble.^{404}^{,}^{405} These methods accelerate the exploration of rough energy
landscapes by allowing some population of high-energy configurations but
still predominantly sample low-energy states, in contrast to simulations at
elevated temperatures. In both cases, it is straightforward to re-weight the
states sampled in order to recover canonical averages. Multicanonical MC was
used by Dinner *et al*.^{165} to interpret fluorescence T-jump experiments for peptide
folding; the Wang-Landau generalization of the method,^{406}^{,}^{407}
which is conceptually similar to adaptive umbrella sampling,^{408} is also now available in the MC module of
CHARMM.^{409}

#### Move Sets

An MC simulation in CHARMM consists of two phases: the choice of a
move set and its subsequent use to generate a trajectory. To optimize
flexibility and speed, these two phases are handled separately. Only a small
number of commands and atom selections are required to construct a move set
because several pre-defined types of moves, which can be combined, are
provided. Certain types of moves can be used with any of the ensembles:
rigid-body translations and rotations of selected sets of atoms and
rotations of dihedral angles individually or in concert.^{410}^{–}^{413} Some moves (e.g., rigid body translations and rotations) can
be linked and applied together.^{395}
Changes to the system volume^{396} and
particle number^{397} are included for
the constant pressure and constant chemical potential methods described
above. Also, MC can call the leapfrog integrator in CHARMM to generate trial
configurations of the system (hybrid MC^{414}^{,}^{415}) in simulations
that sample states with Boltzmann or Tsallis statistics. A self-guided form
of hybrid MC is available^{416} (see
Section VII B).

For each type of move, it is necessary to specify the maximum
extent the system can change in one step and the relative frequency of
application. The allowed step sizes can be adjusted for individual moves
automatically using the acceptance ratio and dynamically optimized MC
methods^{417} (see Hu *et
al.*^{395} for a discussion
of their impact on detailed balance). Hu *et al.*^{395} determined the target acceptance
rates that yielded the most rapid exploration of configuration space for
different types of moves for peptides and found that they ranged from
20% to 95%, in contrast to the conventional belief
that 50% yields the most efficient sampling. These authors went
on to adjust the frequencies of applying different types of moves with a
heuristic MC procedure to obtain peptide move sets that outperformed MD.
Comparison of these move sets makes clear that the optimal values of move
set parameters differ from one system to another. Hopefully, exploration of
MC move sets for other systems at a similar level of detail will lead to
“rules of thumb” for different classes of
biomolecules.

#### Monte Carlo Minimization

With the exception of hybrid MC moves, any of the moves described
above can be followed by minimization prior to application of the acceptance
criterion.^{418} Although this
approach does not satisfy detailed balance, it is useful for applications
like structure prediction and ligand design. Either the steepest descents or
the conjugate gradient minimization algorithms can be employed. The former
is preferable in most circumstances since it is much faster and the primary
function of the minimization is to eliminate steric clashes. An alternative
implementation that exploits the dihedral angle biasing method of Abagyan
and Totrov^{419} and allows simulated
annealing prior to applying the acceptance criterion is also available in
CHARMM (the Monte Carlo Minimization/Annealing or MCMA method.^{143}^{,}^{418})

### E) Grid-Based Searches

As an alternative to the Monte Carlo approach, energy-based searches of
conformational space can be carried out in a systematic and/or deterministic
manner. Such an approach has proven useful for energy mapping of protein side
chain rotational angles and side chain structure prediction,^{45}^{,}^{46}^{,}^{420}^{–}^{422} as well as tertiary structure prediction of
proteins, given the known secondary structural elements^{175} (Petrella, R.J., in preparation.) The Z Module in
CHARMM (keyword ZEROM) generalizes this type of approach to facilitate various
types of grid-based calculations by partitioning the conformational space into
subspaces and systematizing the search. It allows for build-up procedures in
which large parts of the system are generated from low-energy conformers of
smaller parts, and for the inclusion of statistical information (i.e., rotamer
libraries). The Z module has recently been used in molecular docking and loop
prediction calculations to predict the structure of the CMV UL44 processivity
factor complexed with a DNA oligomer.^{423}

## VI. Biased Sampling and Free Energy Methods

Thermodynamic and kinetic properties of a system such as free energy
differences, reaction paths, and conformational free energy surfaces can be
calculated, in principle, from sufficiently long and detailed MD simulations in an
appropriate ensemble. In practice, more elaborate schemes, many of which involve
non-physical states of the system, often can be used to reduce the required
computational time. Some of the approaches have been used in CHARMM since its
inception, while others have been introduced more recently. One important example
appears in the methods for calculating free energy differences between different
thermodynamic states of a system by simulating non-physical
“alchemical” transformations.^{125}^{,}^{424}^{–}^{427} The methods
used to perform computational alchemy have a rigorous basis in statistical
mechanics, and they represent extremely powerful tools for exploring quantities that
correspond to experimental observables, while avoiding the need for prohibitively
costly computations. A number of techniques are summarized here; they include free
energy simulation methods, simulations in four-dimensional space, multiple copy
simulations, and discretized Feynman path integral methods. Umbrella sampling, as
used to speed up convergence of estimates and to determine potentials of mean force,
and computational methods specifically designed to treat conformational transitions
and reaction pathways are described in Section VII.

### A) Free Energy Methods

The core of any free energy simulation methodology is a hybrid
potential energy function U(**r**,λ), which depends on the
so-called coupling parameter, λ. In the simplest case of a linear
dependence on λ,

where U_{0}(**r**) is the part of the potential
energy that does not change, U_{i}(**r**) contains the energy
terms unique to the initial state *i*, and
U_{f}(**r**) contains the energy terms unique to the final
state *f*. For values of the coupling parameter
0≤λ≤1, Eq. (10) can describe the initial
(λ=0), final (λ=1) and
unphysical (“alchemical”) intermediate states of the
system. Because the convergence of the free energy depends on the size of the
change between two states, it is generally necessary to proceed in a step-wise
fashion from the initial to final systems, by utilizing alchemical intermediate
states.

Three different modules, BLOCK,^{428} TSM,^{429}^{,}^{430} and PERT, which were all introduced
*circa* 1986, are available within CHARMM for performing free
energy computations. They make it possible to calculate the free energy
difference between two systems having different potential energy functions,
U_{i} and U_{f}, such as two inhibitors bound to an enzyme
active site. ^{125}^{,}^{424}^{,}^{426}^{,}^{427}^{,}^{431}^{–}^{436} With any
of the three methods, free energy differences can be computed by both
*thermodynamic integration* (TI)^{437} and the *exponential formula*,
often also referred to as thermodynamic perturbation (TP).^{438} For TI, the (Helmholtz) free energy difference,
*ΔA*, between the initial (*i*)
and final (*f*) states is given by:

where the * _{λ}* symbol denotes the ensemble average over the canonical distribution
corresponding to λ. For thermodynamic perturbation (i.e., the
exponential formula),

where
Δ*U*(*λ _{i}*)=

*U*(

*λ*

_{i}_{+1})−

*U*(

*λ*) is the energy difference between the perturbed (

_{i}*λ*

_{i}_{+1}) and unperturbed (

*λ*)system at the

_{i}*i*th value of λ,

*n*is the total number of sampling windows,

*λ*

_{0}= 0,

*λ*=1, and

_{n}_{λi}denotes the ensemble average over the canonical distribution at

*λ*. The two approaches are formally equivalent.

_{i}^{7}

TI can be carried out by windowing, i.e., by performing discrete
simulations with specified values of λ. The ensemble averages are
then calculated for each window and the integration is done numerically, e.g.
using the trapezoidal rule. Alternatively, TI can be performed by slow-growth
(SG), in which λ is varied gradually over the course of a single
simulation.^{439} While the use of SG
has been discouraged because of the “Hamiltonian lag”
problem,^{436} SG-type calculations can
be utilized to carry out so-called “fast-growth”
simulations in combination with the Jarzynski equality; ^{440}^{,}^{441} see
also below. In both the TI or TP methods, the coupling does not need to be
linear. Any smooth functional form in λ can be used, provided
λ is varied slowly enough. Non-linear coupling has been used to
overcome the endpoint singularity problem (van der Waals endpoint problem; see
below). ^{442}^{–}^{444}

The entropy and energy contributions to a free energy change can also
be determined. One way is to calculate the free energy at several temperatures
and evaluate the temperature derivative by finite differences, as in a
laboratory experiment.^{445}^{,}^{446} An alternative, but related, method is
to perform a direct evaluation of the derivatives of the partition function by
finite differences in a single simulation.^{447} In CHARMM, this is implemented in the TSM module.

Detailed analysis based on statistical mechanics shows that several
choices for U(**r,**λ) can be used to compute the free
energy difference, leading to a number of different computational schemes for
performing free energy simulations.^{424}^{,}^{436}^{,}^{448} While all three free energy modules in CHARMM are
based on Eq. (10), at least in
basic mode of operation, the only formal requirement for the functional form of
U(**r,**λ) is that it obey the boundary conditions
U(**r,**λ=0)=U_{i} and
U(**r,**λ=1)=U_{f}. The
different realizations of U(**r,**λ) give rise to the
primary differences among the three modules; in particular BLOCK and TSM use a
so-called dual-topology approach, and PERT uses a single-topology approach^{448}^{–}^{450}

#### The BLOCK Module

The BLOCK module^{428}
provides a general method for scaling energies and forces between selected
groups of atoms. While originally designed to facilitate the computation and
analysis of free energy simulations, the same framework can be used in other
applications for which systematic manipulation of relative strengths of
interactions is required, for example in conjunction with the general
REPLICA module (Section VI C). It also provides the basis for
*λ-dynamics* (see below) and
*chaperoned alchemical free energy simulations*.^{451}

Since, as mentioned, BLOCK adopts the dual-topology approach, the parts of the system which are not the same in the initial and final state have to be defined simultaneously. The hybrid potential energy function in BLOCK can be written as

The coordinates **r**_{0},
**r**_{i}, and **r**_{f},
respectively, are associated with the atoms that do not change, those that
are present only in the initial state, and those that are present only in
the final state. When setting up a free energy simulation using BLOCK, the
user first has to assign the atoms in the system into
“blocks,” according to these three categories. For
example, in the simulation of the mutation of a single protein side chain,
atoms common to the wild type and mutant might be assigned to block 1 (atom
coordinates **r**_{0} in Eq. 13), atoms unique to the wild type to
block 2 (atom coordinates **r**_{i} in Eq. 13), and atoms unique to the mutant
to block 3 (atom coordinates **r**_{f} in Eq. 13). Next, the user has to define
interaction coefficients to describe the interactions within each block and
between each pair of blocks. Through the combination of atom assignments
into blocks and the setting of the interaction coefficients, the user
realizes the hybrid potential energy function (Eq. 13). Optionally, specific energy
terms can be omitted from this partitioning or scaled differently. This
capability is important, for example, in the correct treatment of bonded
interactions in alchemical dual-topology free energy simulations.^{449}^{,}^{450}^{,}^{452} These scaled
interactions (energies and forces, but not second derivatives) are used for
subsequent operations, such as energy evaluation, minimization, and
molecular dynamics simulation. In practice, the user carries out a series of
simulations at a set of λ values. The trajectories saved during
the MD simulations can then be analyzed using special tools provided within
the BLOCK facility to extract and average the quantities of interest, e.g.,
(cf. Eq. 13),
<U/λ>_{λ}=<U_{f}(**r**_{0},**r**_{f})
−
U_{i}(**r**_{0},**r**_{i})>_{λ}
for TI. This analysis is extremely efficient (only a small fraction of all
the interactions in the system need to be evaluated) and can be run
repeatedly to obtain component contributions (i.e., estimates of the
contribution of different parts of the system) to the free energy change.
Near the endpoints (λ = 0 or 1), van der Waals
singularities can cause convergence problems^{453}, which can be circumvented with the use of a soft core
potential (see below). The BLOCK facility also has built-in functionalities
for carrying out slow-growth free energy simulations. Several publications
provide illustrative applications of the BLOCK facility.^{223}^{,}^{428}^{,}^{449}^{,}^{450} Yang *et al.* used free energy
simulations with BLOCK to develop a detailed mechanism for F1F0-ATP
synthase. ^{14} BLOCK was also used in a
study analyzing how DNA repair proteins distinguish the mutagenic lesion
8-oxoguanine from its normal counterpart, guaninine.^{454} Because of its generality, the module
continues to form the basis for new methodological developments (see also
below).

#### The TSM Module

The thermodynamic simulation methods (TSM) module^{429}^{,}^{430}
was developed concurrently with the BLOCK facility to implement TI- and
TP-based free energy methods. TSM, like BLOCK, partitions the system into
multiple components (“reactants”,
“products,” and the
“environment”) and permits simulations to be carried
out either for a fixed value of λ or in slow-growth mode. While
mostly a dual-topology method, one so-called collocated atom can be shared
between the reactant and product state. Conformational free energy surfaces
can be constructed within the TSM framework.^{430} Applications of the TSM-based methods include
protein-ligand,^{455}^{,}^{456} protein-DNA^{457}^{,}^{458}
interaction free energies, and conformational free energies.^{430}^{,}^{459}

#### The PERT Module

The PERT module can be used to calculate alchemical, as well as
conformational free energy differences. In contrast to the BLOCK and TSM
free energy modules just described, PERT uses a single topology-type hybrid
potential energy function U(**r**; λ).^{448}^{,}^{449}
All energy terms, therefore, involve the same coordinate set **r**;
i.e., the energy function has the form of Eq. (10), rather than Eq. (13). Although the energy in PERT has
a linear dependence on λ, in accord with Eq. (10), a variant of the method employs
a “soft core” potential (see below). In the case of
an alchemical free energy mutation in which the number of atoms is not the
same in the initial and final states, so-called
“dummy” atoms must be introduced.

A PERT calculation is initiated by specifying the part of the
system to be subjected to the alchemical mutation. This information is used
to construct three non-bonded pair lists: one each for (i) interactions in
the unchanged part of the system, (ii) interactions with and within the
initial state, and (iii) interactions with and within the final state. The
separate lists are needed for efficiency so that non-bonded terms between
atoms in the unchanged part of the system are only computed once. Bonded and
restraint energy terms, on the other hand, are computed twice, once for the
initial state,
U_{0,bonded}(**r**)+U_{i,bonded}(**r**),
and once for the final state,
U_{0,bonded}(**r**)+U_{f,bonded}(**r**).
(The computational overhead of computing
U_{0,bonded}(**r**) twice is acceptable since calculation
of bonded interactions is computationally inexpensive.) The initial PSF, as
well as the harmonic, dihedral angle, NOE and general geometric (GEO option
of the MMFP module) restraint lists, are saved as the initial state
(λ = 0). The PSF and the three types of restraints
can then be modified to effect the alchemical mutation and/or a
conformational change leading to the end state (λ =
1). The command *MKPRes* can be used to automatically
generate the PSF patch defining the hybrid residues that are needed for
carrying out alchemical free energy simulations. In a procedure that has
similarities with both the single- and dual-topology approaches in free
energy calculations of mutations, the command defines hybrid residues
containing dummy atoms in such a way that all covalent bond contributions
are held constant throughout the calculations and only the non-bonded
interactions are altered. Use of this command avoids the cumbersome (and
error-prone) process of modifying the PSF manually.

When PERT is active, energy calculations, minimizations, normal
mode calculations and molecular dynamics simulations can be carried out for
any value of λ, 0≤λ≤1. In MD
one can specify the change of the coupling parameter as a function of
simulation length, as well as how many steps are used for (re-)
equilibration vs. accumulation of the respective ensemble averages required
for TI and TP. A λ schedule file can be read which allows
explicit control of λ windows. This schedule is usually
determined from a short exploratory simulation so that the fluctuation of
the energy difference in any given window is on the order of
*k _{B}T*. PERT computes the quantities
required to compute free energy differences by TI and TP “on the
fly,” so that in normal usage no post-processing of trajectories
is needed.

PERT includes all contributions resulting from alchemical changes
of bonded energy terms.^{449}^{,}^{450}^{,}^{452}^{,}^{460} Special
attention is required if SHAKE^{354} is
applied to bonds that have different lengths in the initial and final state.
Following an approach outlined by van Gunsteren *et
al*.,^{461} constraint free
energy contributions are computed using a modified SHAKE routine.^{450} PERT runs in parallel and supports
SSBP and GSBP, as well as the Ewald-based methods for computing
electrostatic interactions. PERT, like BLOCK, can produce an atom-based free
energy partitioning that provides useful insights when comparing similar
free energy simulations.^{462} PERT has
also been used in methodological studies focusing on the treatment of bonded
interactions in alchemical free energy simulations,^{449}^{,}^{450}^{,}^{452} as well as in an
analysis of the effect of conformational substates on the precision and
accuracy of free energy estimates.^{463} In addition, PERT has been employed in several
application-oriented studies. A set of optimal atomic radii for PB continuum
electrostatics has been developed via a series of charging free energy
computations executed with PERT.^{192}^{,}^{193} Deng and Roux
computed hydration free energies of amino acid side chain analogs.^{76} The calculated values are in good
agreement with experiment^{464} and
with the results of a more involved approach.^{75} Boresch *et al.* computed relative solvation
free energy differences of phosphophenol derivatives;^{462} the results help to explain the binding
affinities of the corresponding phosphotyrosine mimetics to protein tyrosine
phosphatase and SH2 domains. Several studies using PERT have been carried
out to determine absolute binding free energies.^{465}
^{76}^{,}^{466}^{–}^{469} The
“virtual bond” algorithm introduced by Boresch
*et al.*^{466} is an
implementation of the double decoupling approach formulated by Gilson and
co-workers^{470} whose derivations
generalized the restraint potential methods previously introduced to
correctly account for the standard state in computing the binding affinity
of small molecules for protein cavities.^{465}^{,}^{471}^{,}^{472} Roux and co-workers have studied absolute
binding free energies in three proteins, the Src homology 2 domain of human
Lck,^{467} T4 lysozyme^{469} and FKBP12.^{468}

#### Comparison of methods

Each of the three modules, BLOCK, TSM and PERT, has different strengths and weaknesses. This subsection attempts to provide some guidance for users in choosing the one that is the most appropriate tool for a given problem.

An important decision is whether to use a single- (PERT) or a dual-
topology (BLOCK, TSM) free energy method. For alchemical mutations of small
to medium complexity (e.g., the change of a methyl group into a hydroxyl
group), single-topology treatments are relatively direct and can be set up
easily. For complicated mutations, particularly those involving changes in
connectivity or ring formation, a pure single-topology approach is not
possible,^{448} and the use of a
dual topology method is necessary. The PERT method, while primarily intended
for single topology applications, can be used in a dual topology mode with
an appropriate set of dummy atoms.^{450}^{,}^{460} In applications
involving combined QM/MM calculations, dual topology has been favored,^{454}^{,}^{473} although single topology calculations using the PERT module
are possible for simple alchemical transformations.^{474}^{,}^{475}
^{474} TSM can be used to calculate
free energy and entropy differences simultaneously. PERT offers the best
support for Ewald summation. PERT requires no postprocessing, which can have
practical advantages in distributed computing environments. On the other
hand, BLOCK is a more versatile energy partitioning tool. For example, it is
relatively straightforward to use BLOCK to compute free energy differences
using Bennett’s acceptance ratio method (BAR)^{476}^{,}^{477}
and generalizations thereof based on Crooks’ theorem.^{478}^{,}^{479}

Many of the free energy methods in CHARMM have been implemented by modifying the standard CHARMM energy routines, rather than introducing new ones. This approach makes the standard routines more complex, but it facilitates the integration of the new methods with pre-existing CHARMM functionality. For example, Ewald summation has recently been introduced in BLOCK (A. van der Vaart, private communication), is partly supported by TSM, and is fully supported by PERT. On the other hand, PERT in some cases requires the generic energy routines, which are not optimized for performance. In addition, the PSSP method (a soft core method; see below) can only be used for selected combinations of non-bonded options. Whether these limitations are relevant depends on the specific requirements of the application.

#### The Weighted-Histogram Analysis Method

Post-processing of information from free energy simulations can be
used to achieve more precise estimates of free energy changes using the
weighted-histogram analysis method (WHAM).^{480}^{,}^{481} WHAM minimizes
the error in the estimates by finding optimal weighting factors for the
combination of simulation data from overlapping windows with an iterative
procedure. It makes use of all the available data in the most efficient
manner, and can be used to calculate any kind of ensemble average based on
the conformations sampled in the simulations^{482} including the potential-of-mean-force along coordinates^{481}^{,}^{483}^{–}^{488} and
free energy differences between different states.^{192}^{,}^{489}^{,}^{490}

#### Soft Core Potentials

In alchemical free energy simulations, the use of a hybrid
potential energy function containing a steep repulsive term (e.g.
*r*^{−}^{12} Lennard-Jones) can result in the
“van der Waals endpoint” problem ^{453}, particularly when the number of atoms
changes in the alchemical transformation and the coupling has a simple
linear form. Near the endpoints (i.e., at λ = 0 or
1), extremely large changes in the forces as a function of λ,
which arise from the repulsive term, can occur between
“overlapping” atoms. Techniques for overcoming this
problem include the use of an analytic approximation^{453} and the introduction of soft-core (SC)
potentials for LJ and electrostatic interactions.^{442}^{,}^{443}
In the soft-core method, the distance *r* between two atoms
is replaced by $\sqrt{{r}^{2}+f(\lambda )\delta}$, where δ is an adjustable parameter; for
energy terms belonging to the initial state
f(λ)=λ, and for energy terms belonging
to the final state f(λ)=1-λ. Several
versions of SC potentials are available for use with the various free energy
modules of CHARMM. The soft-core method of Zacharias *et
al.*^{442} is implemented
in PERT for LJ and electrostatic interactions in the
PERT-separation-shifted-potential (PSSP).^{452}^{,}^{491} The PSSP method
has been used in calculations of absolute binding free energies.^{466} A corresponding method can be used
with the BLOCK module.^{492} A related
soft-core technique, based on the Weeks-Chandler-Andersen separation^{493} of the repulsive and attractive
part of the LJ potential, is also available.^{76}^{,}^{467}^{–}^{469} Simulations in 4-dimensional space
can also reduce the endpoint singularity problem in free energy simulations
(see below).^{443}^{,}^{494} See also reference ^{494a}.

#### Free Energy Calculations with λ-Dynamics

A methodology called λ-dynamics has been developed and
implemented in CHARMM.^{495}^{,}^{496} It extends the free energy
perturbation approach by adding multiple variables to control the evolution
of interactions; these variables compete to yield the optimal free energy
for the conformation and chemical configuration of a group of ligands with a
common receptor. The approach builds on ideas put forward by Jorgensen and
Ravimohan,^{497} Liu and
Berne,^{498} and Tidor.^{499} In λ-dynamics, a
hybrid Hamiltonian (potential), somewhat like that in Eq. (10) for free energy simulations, is
used to effect a change of one set of chemical parameters into another via a
pathway that depends upon a number of coupling variables,
{λ_{i}}. In this way, the
alchemical mapping of one molecule into another differentially scales the
components of the solute-solvent interaction terms. One can also consider
multiple chemical species, each coupled to a different λ
variable as described in Eq.
(14), or multiple chemical functionalities on a chemical
framework. If there are *n* types of parameters that are
transformed in the overall mapping, and if the transformation of each is
controlled by one λ variable, i.e., one member of the set {λ* _{i}*}, then the mapping between two molecules may be
achieved through the definition of a Hamiltonian of the general form

where *H _{Env}* includes the kinetic
and mutual interaction energy of the atoms which are not being transformed
(the environment atoms) and

*H*=

_{R(P)}({λ_{i};i*1,n})*denotes the reactant (product) Hamiltonian composed of three elements: the kinetic energy of the reactant (product) atoms, the self potential energy of the reactant (product) atoms, i.e., the reactant-reactant (product-product) interaction energy, and the potential energy of interaction between the reactant (product) and the environment atoms.

*H*is a valid mapping for use in free energy simulations if the endpoints, where

_{Rxn}({λ_{i}})*{λ*=

_{i}}*{0}*and

*{λ*=

_{i}}*{1}*, correspond to the Hamiltonians for the reactant and product states, respectively. The elements in the

*{λ*vector can take on arbitrary, and independent, values in intermediate regions. To achieve maximum efficiency in sampling in the λ-space, the suggestion of Liu and Berne was followed and an extended Hamiltonian,

_{i}}^{331}

^{,}

^{500}which contains the set

*{λ*as dynamic variables, is employed in the CHARMM implementation. The coupling between spatial coordinates and energy parameters is through the λ dependence of

_{i}}*H*. This Hamiltonian has parallels to that used by the Pettitt group to explore thermodynamics in the “Grand” ensembles.

_{Rxn}^{500}From the extended Hamiltonian, the equations of motion for the extended system are readily derived.

^{331}An alternative implementation of the extended Hamiltonian method

^{501}which also uses the lambda parameter as a dynamical variable, relies on thermodynamic integration to obtain the free energy difference. Trial applications indicate that a more rapid convergence is achieved than with the standard TI approach due to dynamic reduction of lambda-coupled conformational barriers in the search space.

Other biases can also be included in the extended system
description. One key element, which enables rapid screening calculations to
be carried out for multiple ligands binding to a common receptor,^{495}^{,}^{502}^{,}^{503} is the
imposition of a free energy bias corresponding to half of a given
thermodynamic cycle; e.g., the solvation free energy for each species can be
added to the extended system Hamiltonian. To compute the relative free
energy of binding of *L* ligands to a common receptor, the
potential energy is defined as

with $\sum _{i=1}^{L}}{\lambda}_{i}^{2}=1$, where each ligand is biased by a constant free energy
term, *F _{i}*, that corresponds to the solvation free
energy of that ligand, the total extent of the ligand-receptor interactions
(presentin the terms

*V*)) is normalized to unity, and

_{i}(X*X*denotes the configuration coordinates of the ligands, solvent and receptor. By carrying out a λ-dynamics simulation of this extended hybrid system and monitoring the probability of each ligand to achieve unit values of λ, the overall free energy change for any pair of ligands is determined from the expression

^{504}

where $\mathrm{\Delta}{A}_{ij}^{Rec}$ is the free energy difference for the half cycle
corresponding to ligands *i* and *j* in the
receptor binding pocket, $\mathrm{\Delta}{F}_{ij}^{\mathit{Solv}}={F}_{i}-{F}_{j}$ is the free energy half cycle corresponding to solvation
of the ligands and was input as a bias in the initial calculations, and $\mathrm{\Delta}\mathrm{\Delta}{A}_{ij}^{\mathit{Bind}}$ is the overall relative free energy change for the binding
competition between ligands *i* and *j*.

#### Some Recent Developments in Free Energy Methodology

Free energy difference calculations, as described above, are being
more extensively utilized in biomolecular simulations. The required computer
time for obtaining converged results is decreasing and the reliability of
the results is improving, even as the processes under study become more
complex. Some important conceptual/methodological advances have been
introduced recently. One new approach, called the MARE method^{478}^{,}^{479} is a general method for estimating free energy changes from
multistate data (such as those obtained in replica exchange calculations;
see also Section VI B) by utilizing all of the simulated data
simultaneously. As an example, simulations are done with replica exchange
for the alchemical transformations of A to A_{1}, A_{2}, and
A_{3}. It is shown that including all of the results in the MARE
scheme significantly reduces the error of each one relative to that using
the data for A to A_{1}, A to A_{2}, and A to A_{3}.
Separately, the formulation reduces the statistical error significantly from
previous estimators. The MARE approach was motivated by the original Bennett
acceptance ratio method,^{476}^{,}^{477}^{,}^{505} which makes use of the maximal likelihood evaluation of a
free energy perturbation from one state to another. Complementing the MARE
method, a λ-WHAM approach has been introduced to refine free
energy derivative histograms with the maximum likelihood method; see^{506}. The efficiency of conformational
sampling for problems where the change in the system is local, as in point
mutations in proteins or in ligand binding, can be improved by the simulated
scaling method^{507} and its replica
exchange version,^{492} in which only
the potential energy of the region of interest is scaled. To realize a
random walk in scaling-parameter space, the simulated scaling method has
been implemented with a Wang-Landau updating scheme and shows rapid
convergence of free energy calculations for model systems.^{507} An extension of this approach to chaperoned
QM and QM/MM free energy simulations^{451} has also been implemented.^{508} The chaperone method uses a molecular mechanics force field
for the quantum region, so that unphysical geometries are prevented in the
λ=0 and λ=1 limits, where
the quantum mechanical terms are small. The methodological improvements that
have been described here are examples of an ongoing effort to broaden the
range of biophysically important problems to which free energy simulations
can be applied.

### B) The MMTSB Tool Set

The exploration of the accessible conformational space required for
thermodynamic analysis can be enhanced through the use of advanced sampling
techniques such as replica-exchange molecular dynamics.^{509} To assist in doing such calculations, as well as
those involving a host of related “ensemble” simulation
methods, the Multiscale Modeling Tools for Structural Biology (MMTSB) set of
perl-based scripts and libraries ^{510} has
been interfaced with CHARMM. This tool set provides a useful complement to
CHARMM for the control and manipulation of large-scale calculations that are
distributed over many computers. One key application in this area is
replica-exchange molecular dynamics, which can be performed within CHARMM. In
this technique, several replicas of the system of interest are prepared and
simulated independently over a range of temperatures (generally exponentially
distributed) and then permitted to exchange with neighbors at intervals chosen
in accord with the Metropolis criterion. This enhances the conformational
diversity of the members of the composite ensemble by allowing low-temperature,
potentially trapped, conformations to access higher temperatures and overcome
barriers. The method has been used together with GBMV implicit solvent to
analyze nucleoside conformational preferences.^{511} Replica-exchange with CHARMM and the MMTSB tool set have been
employed in the study of protein and peptide folding, structure prediction and
refinement, and membrane-influenced peptide folding, insertion and
assembly.^{132}^{,}^{137}^{,}^{229}^{,}^{302}^{,}^{512}^{,}^{513}
Figure 5 illustrates two recent examples of
the application of replica-exchange sampling with implicit solvent models based
on the GB methodology discussed above.^{514}

### C) Enhanced Sampling via Multiple Copy Methods

Multiple copy methods make possible the enhancement of phase space
sampling for a subset of variables of interest (e.g., selected amino acid side
chains in a protein), in the context of a surrounding set of such variables or
bath (e.g., the remainder of the protein). The inspiration for these methods is
based on the time-dependent self-consistent field approximation, a mean field
approach developed for the study of dynamical properties in electronic structure
calculations.^{515} The first
application of a multi-copy method to biomolecular systems was the locally
enhanced sampling (LES) method introduced by Elber and Karplus^{516} in a study of ligand diffusion in myoglobin.
Trajectories were simultaneously propagated for multiple copies of the ligands,
but for only one copy of the protein, so as to greatly reduce the computational
cost of the calculation. A similar approach is now commonly employed to
determine which chemical functional groups have a favorable interaction with
protein binding sites. The multiple copy simultaneous search method (MCSS)^{517}^{,518}^{,}^{519} floods the active
site with multiple copies of small chemical fragments and then performs
simultaneous energy minimization or quenched dynamics to find local minima for
the different ligands on the receptor-ligand interaction potential energy
surface. Using a set of ligands allows the generation of functionality maps for
the characterization of intrinsic binding site properties; these maps can
subsequently be used as the basis for ligand and combinatorial library
design.^{519}^{–}^{522} Most of the applications have employed
a rigid protein model, in which case the multiple copy approach is a
book-keeping convenience relative to the execution of multiple, separate runs.
However, an extension of the MCSS method allows the use of a flexible protein,
in which case a significant sampling efficiency is realized.^{518} The MCSS approach has inspired the analogous
experimental approaches of Multiple Solvent Crystal Structures^{523} and Structure-Activity-Relationships by NMR.^{524} A comparison of the experimental and
simulation approaches has been described.^{525} Because of its widespread utility in pharmacological research,
the MCSS methodology is distributed as a separate program which makes use of
CHARMM. The multiple copy approach has also been employed in a number of
conformational sampling problems such as the optimization of local side chain
conformation,^{526} and the global
prediction of peptide conformation.^{323}
Attempts to derive thermodynamic properties from multi-copy simulations have
been made,^{527} and a number of studies
have been carried out to address the meaning of the temperature in the
simulations and the appropriate treatment of the ensembles involved.^{528}^{–}^{531}

#### The REPLICA Module

Both LES and MCSS can be activated using the
*REPLica* command, which is one of the fundamental system
generation and modification facilities in CHARMM. The
*REPLica* command was originally implemented so as to
support a class of methods that seek to improve the conformational sampling
of a (usually small) region of the molecular system by selective
replication. In principle, its function is to allow the specification of a
part or parts of the molecular system through an atom selection, and to
generate a specified number of copies (or replicas) of the selected
subsystem’s attributes (i.e., topological, structural and
selected physical properties). Conceptually, each set of replicas
constitutes a separate subsystem that is distinct from the primary system.
The *REPLica* command can be issued repeatedly to create
multiple subsystems. The key effect of the command is in the non-bonded pair
list generation routines, which underpin the calculation of the non-bonded
interactions in the energy function. Atoms in different replicas within the
same subsystem are excluded from the non-bonded pair list and thus do not
interact with each other. Replicas in different subsystems do interact, with
appropriate mass and interaction scaling as specified using other CHARMM
facilities (e.g., BLOCK, Section VI A, and SCALAR, Section II C). Additional
functionality has been built upon the REPLICA formalism in CHARMM to support
the location of transition states and the estimation of discretized Feynman
path integrals (Section VI D).

### D) Discretized Feynman Path Integrals

While quantum mechanical calculations have an essential role in the
evaluation of classical semiempirical potential energy surfaces (see Section III
E) and the study of chemical reactions and catalysis (see Sections III E and VII
F), the inclusion of quantum effects can also be important in the calculation of
the equilibrium properties and dynamics of a system, particularly at low
temperatures, where the effects can be significant.^{24}^{,}^{348}
Quantum effects on equilibrium properties can be investigated by exploiting the
isomorphism of the discretized Feynman path integral (DFPI) representation of
the density matrix with an effective classical system obeying Boltzmann
statistics.^{532} According to this
approach, an effective classical system is simulated in which each quantized
particle is replaced by a classical ring polymer, or necklace, of
*P* fictitious particles (“beads”)
with a harmonic spring between nearest neighbors along the ring; each bead
interacts with two neighbors and the last bead interacts with the first. The
spring constant decreases as a function of temperature and mass of the nuclei,
giving rise to more extended ring polymers, which correspond to the DFPI
manifestation of familiar quantum effects, such as zero-point vibration and
tunneling. Molecular dynamics or Monte Carlo simulations of the effective
classical system (in which some or all the particles are described by isomorphic
ring polymers) are valid for obtaining ensemble averages, although they do not
provide information on the time-dependent quantum dynamics of the system.

In the current CHARMM implementation of DFPI, each quantum atom is
represented by the same number of beads.^{533} The creation of the beads utilizes the REPLICA facility
described above. The energy of the ring polymers is a sum of harmonic terms
between consecutive beads along the necklace with spring constant
*K _{DFI}* =

*Pk*

_{B}

*T/*Λ

^{2,}where Λ is the de Broglie thermal wavelength of the quantum particle Λ = (

*h/*2

*π*)

^{2}(

*mk*

_{B}

*T*). These interactions are added to the CHARMM energy through the command

*PINT*. The interaction with other atoms is introduced by means of the classical CHARMM potential energy function scaled by

*1/P*; each bead interacts only with one bead in other quantum atoms, and there is no interaction between beads belonging to the same necklace, except for the spring interaction within the necklace. The attribution and scaling of the different interactions is specified with the

*BLOCk*command.

^{533}

### E) Simulation in 4-Dimensional Space

The addition of a nonphysical fourth spatial dimension to molecular
mechanics can increase the efficiency of sampling conformational space.^{352} Enhanced sampling of conformations is
achieved because barriers in the physical (3-dimensional) space can be
circumvented by introducing the higher dimensionality of 4 spatial dimensions.
Energy and forces are computed in 4D by adding a fourth value,
*w*, to the atomic coordinates (*x,y,z*); in
CHARMM, this is done through the use of the VER4 dynamics integrator (see also
Section V B). After initial assignment of the 4D coordinates and velocities, a
harmonic energy term allows control of the embedding of the system in the fourth
dimension; an increase in the associated force constant of this term leads to
smaller *w* values, thereby projecting the system into 3D space.
Molecular dynamics in four dimensions has been applied to problems related to
protein structure determination^{534}^{,}^{535} and free energy calculations.^{443}^{,}^{494} MD in 4D space searches a large enough conformational radius to
allow the use of random-coil configurations for initial coordinates.^{536} The use of a fourth spatial dimension
has been shown to be advantageous for calculating free energies of solvation and
of ligand binding affinity whereby the solute non-bonded interactions are
coupled to the system through *w*, and a potential of mean force
(4D-PMF) is calculated by umbrella sampling over the range
*w*=0 to *w*=1
corresponding to the reversible abstraction of the solute from the solvent or
binding site.^{494}^{,}^{537} In these studies, the approach resulted in
accurate solvation free energy estimates, and converged efficiently without the
van der Waals endpoint problems experienced with λ-scaling of
non-bonded interactions (see Section VI A). The 4D-PMF method is simple to
implement because it is easily generalized to all Lennard-Jones and Coulombic
non-bonded interactions.

## VII. Reaction Paths, Energy and Free Energy Profiles

An important problem in molecular modeling is the determination of the
minimum energy or free energy pathway and the transition rate between two different
conformations. Many biomolecular processes involve large-scale conformational
changes in the structure of the system.^{13}^{,}^{300}^{,}^{538}^{,}^{539} Often the
transition is a rare event, occurring on a timescale well beyond the reach of
conventional MD (on the order of 100ns or longer for large systems). Consequently,
specialized approaches must be used to observe such transitions in simulation.

Several simulation methods have been developed to determine minimum energy and free energy pathways on multi-dimensional potential surfaces of complex biomolecules. These methods vary in the details of the path sampling procedures they employ, whether they use reaction coordinates, and, for those that do, the types of reaction coordinates for which they are best suited. Reaction coordinates are the degrees of freedom, or functions thereof, by which the pathway is defined. For many calculations, they are a small number (1 to 3) of geometric parameters (e.g., RMSD between initial and final states, certain bond angles), but can include order parameters of any type (e.g., fraction of native contacts, number of hydrogen bonds) or number. The term “reaction path,” which originated in the study of chemical reactions, is now used more generally to refer to the pathway of a molecule between two end states in conformational or chemical space. Both the minimum energy path (MEP), which provides the energy, and the potential of mean force along a path, which provides the free energy, can be calculated with CHARMM.

The MEP is the path on the potential surface that connects the reactant
state to the product state (or two intermediate states if there is a multibarrier
transition) by steepest descent from the barrier, or saddle-point, which is the
stationary point where the Hessian matrix has a single negative eigenvalue. MEPs
provide a useful description if the free energy along the path is dominated by the
enthalpy; changes in the vibrational entropy along the path to obtain the free
energy can be included *a posteriori.*^{540} For processes involving important changes in
conformational entropy, the MEP can provide a curvilinear reaction coordinate along
which the PMF can be computed.^{48} A
chain-based method (i.e., one that optimizes the entire path simultaneously) was
originally developed by Elber and Karplus;^{541} a refinement of the method is referred to as the
“self-penalty walk method”^{542} and the Replica Path method in CHARMM is based upon it and the
REPLICA code. Several other chain-based MEP methods have been developed subsequently
— e.g., the Nudged Elastic Band (NEB) method^{543}^{,}^{544} and the
Zero-Temperature String (ZTS) method.^{544}^{–}^{546} All of these
methods find a locally optimized path, which is not necessarily the global optimum
path; this is a general problem with optimization methods for complex systems.
Existing MEP calculation methods include automatic search methods for improving
pathway exploration and the location of the globally best path.^{547}

Under physiological conditions, molecules can cross low-energy barriers,
and more than one transition path can contribute significantly to the transition
rate.^{166} Hence, a related problem is
finding an ensemble of paths or the best average (minimum free energy) path at
non-zero temperatures. One approach makes use of non-equilibrium methods available
in CHARMM. It requires that stable states of the reaction are known from experiment,
and that suitable order parameters that characterize these states and the distance
of a conformation from them can be defined. In such cases, insights into the
reaction path can be gained from multiple trajectories generated with targeted or
steered molecular dynamics approaches.^{142}^{,}^{548}^{–}^{553} The various methods differ with regard to
the form of the bias, which can be either a holonomic constraint or a restraining
term added to the energy function, and the schedule with which it is advanced. As a
rule, methods that advance the bias more slowly and apply smaller biasing forces are
less likely to give rise to dynamic artifacts.^{401} Self-guided stochastic methods^{416}^{,}^{554} can be useful for
exploring the available free energy basins and the paths connecting them in cases
where the final state is not known.

The PMF along some chosen reaction coordinate plays a central role in
modern transition state theory and its generalization to many-body systems.^{555} It can be used to evaluate a transition
rate, the dynamical prefactor, and the transmission coefficient. Special biased
sampling techniques can be used to calculate these quantities from a molecular
dynamics trajectory. In particular, the PMF can be calculated using the free energy
perturbation technique^{438} (see section VI
A), the umbrella sampling technique (see Section VII C), ^{556} or the Jarzynski equality.^{440}

The transmission coefficient can be calculated using the activated dynamics
procedure;^{555}^{,}^{557} an early example of its application to a biologically
interesting system is given in Northrup *et al.*^{296} Alternatively, it is possible to estimate the
transmission coefficient in the diffusive limit using an analysis based on the
Generalized Langevin Equation.^{558}^{–}^{560} More
generally, transition path sampling methods^{395}^{,}^{561}^{–}^{563}
^{401} sample the dynamics of a system without
bias but require harvesting many trajectories of lengths comparable to the time it
takes for the system to relax from the transition state to a stable state (the
“commitment time”).

The fundamental importance of determining chemical and physical reaction
mechanisms has naturally led to the introduction of many methods for finding
reaction paths, as is made clear by the discussion in this section. In general,
there is a tradeoff between the computational resources required by methods and the
accuracy of the description that they provide. Thus the choice of method depends on
the system of interest and the goals of the investigator. In all of the reaction
path methods, care must be taken in the labeling of chemically equivalent atoms
(e.g. the two δ position atoms or the two ε position atoms
in a benzyl ring) in all of the copies, so as to avoid introducing artifactual
dihedral angle rotations into the path.^{564}
This problem often arises when the starting or end structures in a calculation are
derived from separate sets of x-ray crystallography data. A facility which relabels
chemically equivalent atoms in two structures according to RMSD criteria has
recently been developed and will be available in future versions of CHARMM.

### A) Chain-based path optimization

The search for a reaction path and the corresponding
transition-state(s) is not straightforward if more than a few degrees of freedom
are involved. Methods that drive the system along a one-dimensional reaction
coordinate (e.g., a torsion angle or the RMS deviation from the product), such
as adiabatic minimization with a restraint or targeted MD (see Section VII.D
later), are straightforward to apply. However, finding the appropriate reaction
coordinate(s) to describe the transition can be difficult, even in apparently
simple reactions. For example, in the *cis-trans* isomerization
of the proline peptide bond, the standard backbone torsion angle ω
was shown to be inappropriate as a reaction coordinate.^{565} An alternative to using a predefined reaction
coordinate is to obtain the MEP by optimizing the entire path as described by a
chain of conformers. This approach requires an initial guess for the path, which
can be as simple as the linear interpolation between the end-states. It is also
possible to include in the initial guess a set of predetermined intermediate
structures, which are then optimized with the rest of the path. The following
three methods in CHARMM use the chain-based path optimization approach.

#### Replica Path Methods

In the original chain-based optimization method of Elber and
Karplus,^{541} an initial guess for
the path can be provided by a linear interpolation between end states, such
that the coordinates of the *j*th point, *R
_{j,}* along the path are given by

*R*=

_{j}*R*

_{0}+

*j*Δ

*R*, where Δ

*R*= (

*R*

_{0}−

*R*

_{M}_{+1})/(

*M*+ 1),

*R*

_{0}and

*R*

_{M}_{+1}are the coordinates of the fixed endpoints, and

*M*is the number of free path points. A first-order minimization method, the Powell algorithm, is then used to minimize a functional of the form

where *V* (*R _{j}*) is
the potential energy of the system at path point

*j*,

*L*is the length of the entire path, Δ

*l*is the length of path segment

_{j}*j*(distance between path points

*j*and

*j+*1), Δ

*l*

*is the RMS path segment length, Δ*

_{rms}*t*

_{j}^{2}is a measure of the rotation and translation of the coordinates of path point

*j*relative to its coordinates at the start of the calculation, and

*λ*and

*λ*′ are parameters. Hence, the potential energy of the entire path is minimized while the path segment lengths (second term) and the global rotation and translation of each path point (third term) are restrained. In the self-penalty walk method,

^{542}

^{,}

^{566}rigid rotation/translation is constrained by a different method and an additional restraint term is added that is of the form $\rho {\sum}_{i>j+1}^{M+1}exp(-{r}_{i,j}^{2}/{({\lambda}^{\u2033}{\langle r\rangle}_{\mathit{rms}})}^{2})$, where

*r*is the distance between two path points,

_{ij}*r*

*is the RMS distance between sequential points, and*

_{rms}*ρ*and

*λ*″ are parameters. This “repulsion” term prevents the path from revisiting the same regions of conformational space. Many current reaction path methods are derivatives of this “self-avoiding” or self-penalty walk method. Methods of this type eliminate the expensive analytic Hessian computation required for the Intrinsic Reaction Coordinate (IRC) method

^{567}, which is generally used in quantum mechanical studies of small molecules. Since the self-penalty walk methods use a differentiable target function, they are well suited for searching and improving paths using high-temperature annealing or self-guided Langevin dynamics

^{554}

^{,}

^{568}for the exploration of the conformational space.

The replica path method^{289}^{,}^{569} is similar in
spirit to the self-penalty walk method, but it utilizes the REPLICA
functionality in CHARMM (Section VI C) to construct a trial reaction path by
replicating the part of the molecule that is involved in the conformational
change. This feature allows a partitioning of the system into replicated
atoms that are directly involved in the pathway and environment atoms whose
positions are the same for all replicas. The method restrains each replica
with a penalty function that uses best-fit RMS distances to the two adjacent
replicas, thereby circumventing the need for restraining the rotation and
translation of the replicas. A restraint on the pathway curvature using the
RMSBFD (root-mean-square best fit distance) metric is included, in lieu of a
temperature-related term used in some other chain-of-states methods, to
smooth the pathway and keep it from folding back on itself. For each path
point (replica), *i*, this restraint term involves the angle,
*α _{i}*, between

*i*,

*i*+1, and

*i*+2; the term is of the form ${E}_{\mathit{ang}}={\sum}_{i=1}^{m}{K}_{\mathit{ang}}{({C}_{max}-cos({\theta}_{i}))}^{2}/2$, where

*θ*=180−

_{i}*α*,

_{i}*C*is the cosine of the angular deviation from linearity above which the restraint is applied,

_{max}*K*is the force constant determining the stiffness of the path and

_{ang}*m*is the number of path points. Customized specification of atomic weighting factors can be also used in the RMSBFD calculation to vary the degree of participation of a given atom in the conformational change metric. Atoms selected with zero weight contribute to the energy in the path calculation, but their displacement is not included as part of the path and they are not used in the application of the restraints.

The replica path method in CHARMM can be used with both classical
and hybrid QM/MM Hamiltonians. Several QM packages may be used in a parallel
scheme (i.e., parallel QM/parallel MM) that can efficiently use hundreds of
processors: GAMESS-UK,^{266}^{,}^{285}^{,}^{570} GAMESS-US,^{286}^{,}^{287} and Q-Chem.^{294}^{,}^{295}
Parallel efficiency is achieved by computing the quantum energy of each
replica in parallel on a different set of processors.^{289}^{,}^{569}
For single-processor calculations, the SCC-DFTB package can also be
used.^{571} The QM/MM replica path
method is an effective tool for obtaining approximate minimum energy
reference pathways. These are obtained either by minimization, or by
calculating an average structure for each replica from a Langevin dynamics
simulation and then optionally smoothing. The smoothed path is useful for
subsequent PMF simulations by umbrella sampling.

A potential problem that can arise with the use of minimum energy
path methods for the study of large systems is that there can be
“uncorrelated” fluctuations in the total energies
due to system motions that are unrelated to the pathway of interest (e.g.,
the rotation of a water molecule that changes the total energy by several
kcal/mol). The replica path method, as well the REPLICA-based NEB method
described next, mitigate this problem by treating the environment
consistently over the course of the entire path, allowing all replicas to
see the same environment. However, the total energy over an optimized
zero-temperature path generated with these methods may still be subject to
uncorrelated fluctuations when the replicated portion, itself, is large. In
these cases, the calculation of the approximate work done over the 0K path
can yield meaningful results. The forces from the entire replicated region
and environment are included in the work term, but because only their
projections along the path contribute, the effect of uncorrelated motions in
the distant parts of the replicated regions is diminished. The
“0K work” term has been shown to converge to the
system energies in the chorismate mutase reaction path for a small
replicated region (6 Å in radius),^{289}^{,}^{569}
For cases in which the replicated region is larger and in which the 0K work
term and the system energies do not agree, the former is the more meaningful
and reproducible quantity. The off-path simulation method (Woodcock H.L.
*et al.,* in preparation) extends this idea to the
computation of PMFs by utilizing a fixed reference pathway and RMSBFD
restraints to define an umbrella potential and allow free motion in planes
orthogonal to the pathway. These planes can be thought of as having an
approximately constant value for the commitment probability. The force
vectors resulting from a simulation using these restraints, along with the
corresponding distance vectors, are rotated into the frame of the reference
pathway for each segment of the path, yielding an average work term, which
may be partially curvature corrected.

#### Nudged Elastic Band Methods

The nudged elastic band (NEB) method^{543} is another chain-of-states method that is
implemented in two different forms as part of the replica path code in
CHARMM. The NEB method determines minimum energy pathways that are locally
exact, given the approximation of using a finite (usually small) set of
replicas. The forces acting on each replica are given by

where *V*(*R _{i}*) is
the potential acting on the

*i*th replica,

_{||}is the pathway tangent vector,

*V*(

*$\stackrel{\u20d7}{R}$*)|

_{i}_{}=

*V*(

*$\stackrel{\u20d7}{R}$*) − (

_{i}*V*(

*$\stackrel{\u20d7}{R}$*)·

_{i}_{||})

_{||}is the projection of the perpendicular component of

*V*(

*R*) and $({\overrightarrow{F}}_{i}^{S}\xb7{\widehat{\tau}}_{\left|\right|}){\widehat{\tau}}_{\left|\right|}$ is the parallel component of the spring force introduced to keep the replicas equally spaced along the chain. The two forms of the method implemented in CHARMM differ in the definitions of the spring force and the tangent vector. In addition, one uses RMS distances to calculate pathway step lengths and angles,

_{i}^{572}and the other uses root-mean-square best-fit distance (RMSBFD) values.

^{297}

In CHARMM, a minimization scheme with superlinear convergence
properties has been developed and implemented for the NEB method.^{297} The algorithm is based upon the
adopted basis Newton-Raphson (ABNR) method. During the minimization, each
ABNR step is performed self-consistently in a user-defined subspace. The
superlinear minimization scheme of NEB has been shown to be more efficient
than quenched molecular-dynamics minimization or steepest descent
minimization.^{297} In addition,
the CHARMM implementation of the NEB method is also able to take advantage
of the RMSBFD pathway definitions (see Section VII A) and to employ flexible
weighting options. Also, because the NEB implementation is coupled to the
REPLICA code, the parallel/parallel QM/MM pathway functionality in CHARMM
can be used to examine bond-forming and bond-breaking processes. In addition
to the standard NEB method, CHARMM also supports the climbing image NEB
(CI-NEB).^{573} In this method,
which is a modification of the original NEB, one of the images is moved to
the highest energy saddle point along the path. The CI-NEB is robust with
respect to the discretization of the pathway and returns an accurate
estimate of the transition state energy. Use of the CI-NEB method following
a standard replica path or NEB pathway calculation can save significant
computer time when the focus is on transition state properties.

Another chain-of-states method is the recently developed string
method^{544}^{–}^{546} and its implementation using
swarms-of-trajectories.^{574} It is
similar in spirit to the NEB method, but the replicas are independent during
dynamics and minimization (no interreplica restraints), and they are
repositioned along the interpolated path after every global iteration. Thus,
the string method is, in principle, somewhat simpler to implement and
parallelize than NEB. Moreover, the finite temperature string method, unlike
NEB, permits the calculation of free energy surfaces. Application has been
made to the solvated alanine dipeptide.^{575}

#### Conjugate Peak Refinement (CPR) Method

Another algorithm for finding the MEP is Conjugate Peak
Refinement,^{576} which is
implemented in the TREK module (keyword TRAVEL) of CHARMM. Starting from an
initial path, CPR finds a series of structures that closely follow the
valleys of the energy surface and determines all saddle points along the
path. Unlike the replica path and NEB methods, the CPR algorithm does not
utilize the REPLICA functionality in CHARMM. Instead, the method replicates
the system internally, and environment atoms can be fixed to reduce the
degrees of freedom in the problem. CPR is capable of determining the
relevant saddle-point(s) along transition pathways that involve tens of
thousands of degrees of freedom. The principle of CPR is to focus the
computational work on improving the high-energy segments of the path. An
iterative procedure is used, and in each cycle the highest local energy
maximum along the path (called the “peak”) is found
and the path is rebuilt so that the new path circumvents the high-energy
region around the peak. This is done by improving, removing or inserting one
path-point. Points that are inserted or improved are optimized by a
controlled conjugate gradient minimization, which prevents each point from
falling into an adjacent minimum and which converges to the saddle-point if
the peak was located in a saddle region of the energy surface (i.e., the
path was crossing over a barrier). The path refinement is finished when the
only remaining energy-peaks along the path are true saddle-points. Because
the number of path-points is allowed to vary during the refinement, and no
constraints are applied on the path shape, any degree of complexity of the
underlying energy surface can be accommodated. The details of this heuristic
algorithm are described in Fischer & Karplus^{576} and in the CHARMM documentation. Since the
parameters of the algorithm are independent of molecular size or the nature
of the reaction, they do not need to be reoptimized for new reactions.
Thorough minimization of the structures is required. Also, to be compatible
with CPR, a potential energy function must have analytic and
finite-difference derivatives which correspond (i.e., must pass *TEST
FIRSt*; see Section XI B). CPR is parallelized and works in
combination with QM/MM implementations and with most Generalized
Born–related continuum solvation methods. For the purpose of
energetic analysis or subsequent PMF calculations along the MEP,^{48} the resulting CPR path can be
effectively smoothed with the Nudged Elastic Band method (see above) or with
the Synchronous Chain Minimization (SCM) method. In SCM, all path points are
simultaneously energy-minimized under the constraint that each point must
remain on the hyper-plane that bisects its two adjacent path-segments; these
planes are periodically updated as the path evolves. To prevent kinks in the
path and the descent of path-points into nearby minima, SCM controls the
change in the angle between adjacent path segments during the minimizations.
SCM is implemented in the TREK module of CHARMM.

Problems to which the CPR algorithm has been applied include: 1)
enzymatic catalysis, where the end-states of the substrate can be either
conformational isomers (e.g., the rotamase FKBP^{577}) or chemically different species (e.g.,
proton transfer in Triosephosphate-isomerase^{254}); 2) the study of membrane channel permeation, where the
substrate in the two end-states can be placed on either side of the membrane
(e.g., sugar-chain translocation across maltoporin^{578}); 3) ligand entry paths into buried binding
sites, which can be explored by using reactant states where the ligand is
placed in various locations on the protein surface (e.g., retinoic acid
escape^{36}); and 4) pathways for
large-scale conformational change between different crystal structures of
proteins.^{579} The robustness of
the CPR method allows it to be used in automatically mapping the
connectivity of complex energy surfaces and, with graph-theoretical
best-path searching algorithms, in identifying the globally lowest path in a
dense network of sub-transitions.^{547}
CHARMM scripts enabling this functionality can be found in the
“support” directory.

### B) Non-Equilibrium Trajectory Methods

Several methods for determining a reaction path between a product and a
reactant follow the non-equilibrium trajectory of the system starting in the
reactant basin while a biasing potential is applied to drive the system towards
the product basin. In most cases, the trajectories generated according to such a
scenario are irreversible; i.e., the system does not necessarily return to the
initial state if the biasing potential is turned off because barriers along the
pathway are usually present in both directions. The resulting trajectories are
generally found to provide useful insights concerning the character of the
transition pathway. Moreover, once a pathway has been calculated, it is possible
to determine the free energy associated with it by umbrella sampling or
alternative methods.^{48} Also, in some
cases the underlying equilibrium PMF can be calculated via the non-equilibrium
approach due to Jarzynski,^{440} though
accurate estimates are difficult to achieve.^{580} A number of such non-equilibrium methods are supported in
CHARMM. They are targeted molecular dynamics (TMD),^{548} self-guided Langevin dynamics (SGLD),^{554}^{,}^{568} steered molecular dynamics (SMD) ^{401}^{,}^{549}^{–}^{551} and the
half-quadratic biased MD (HQBMD) method.^{142} In addition to these specialized non-equilibrium methods, CHARMM
provides a number of general potential energy restraints (described in Section
III F), along with a dedicated restraint facility called RXNCOR, that can be
used to control the progress of a trajectory.

#### Targeted Molecular Dynamics (TMD)

In 1993, a constrained dynamics method called targeted molecular
dynamics (TMD) was developed to simulate the pathways of conformational
transitions of biomolecular structures that occur on time scales much longer
than are accessible in conventional MD simulations.^{548} If the atomic structures of two conformations
of a protein are known, this method can be used to identify a transition
pathway from a starting conformer to the target conformer by applying a
single time-dependent holonomic constraint based on the (mass-weighted) RMSD
between the two conformers. The general form of the constraint is

where *N* is the number of atoms in the
system, *$\stackrel{\u20d7}{x}$ _{i}*

_{,}

*is the position of atom*

_{F}*i*in the target conformer,

*$\stackrel{\u20d7}{x}$*(

_{i}*t*) is the position of atom

*i*at time

*t*,

*η*(

*t*) is the desired mass-weighted RMSD between the system and the target structure at time

*t*,

*m*is the mass of atom

_{i}*i*, and

*$\stackrel{\u20d7}{X}$*= {

*$\stackrel{\u20d7}{x}$*

_{1},

*$\stackrel{\u20d7}{x}$*

_{2},…

*$\stackrel{\u20d7}{x}$*}. At each step of the MD simulation, the system is first allowed to evolve according the physical (unperturbed) potential energy function. The constraint forces, ${\overrightarrow{F}}_{i}^{c}=\partial \mathrm{\Phi}/\partial {\overrightarrow{x}}_{i}$, then perturb the structure so as to satisfy Eq. (19); for each atom, the force is proportional to the difference between the atom’s coordinates in the current and target structures — i.e., ${\overrightarrow{F}}_{i}^{c}(t)\propto ({\overrightarrow{x}}_{i}(t)-{\overrightarrow{x}}_{i,F})$. Application of the constraint (with the mass weightings) conserves the position of the center of mass of the system, provided that the centers of mass of the current and target conformers are the same and all of the atoms are included in the constraint. Although the method imposes no

_{N}*a priori*restrictions on the time-dependence of the constraint parameter

*η*(

*t*), which controls the rate of convergence of the initial conformer to the target, the parameter is commonly made to decrease linearly with time (but see RP-TMD below), until it reaches a user-defined tolerance. As an alternative to this type of holonomic constraint, a harmonic restraint can be used in TMD.

^{552}

In CHARMM, the TMD constraint can be based on all atoms or a chosen
subset of atoms (second atom selection in the *TMD* command);
the remaining degrees of freedom in the system are allowed to relax
according to the physical potential energy surface throughout the
simulation. If the atom selection (typically, the protein mainchain atoms)
does not include all the atoms in the system, application of the constraint
does not in general preserve the center-of-mass of the system. Since the
holonomic constraint employed in TMD does not conserve angular momentum, the
target structure can be superimposed onto the simulated structure by a
least-squares fit at a user-specified frequency (by use of the
*INRT* option and the first atom selection in the
*TMD* command) so as to remove overall rotation. The TMD
constraint can be used in conjunction with other CHARMM constraints such as
SHAKE, which fixes bond lengths. As with other methods that introduce
external forces, the use of Langevin dynamics is recommended with this
method to control the temperature so as to obtain smooth trajectories. TMD
permits simulations to be performed at any desired temperature; this is an
advantage in the study of biomolecules and other systems with significant
entropic contributions, since pathways generated at ambient temperature are
often more realistic than the minimum-energy pathway. The TMD method in
CHARMM has been widely used. An example is the determination of the reaction
paths for the transition between the GTP-bound and GDP-bound conformations
of the molecular switch I and II regions of oncogene protein
p21^{ras},^{581} which
recognize distinct sets of partner proteins on the cell signal transduction
pathway.^{582} An interaction that
occurs along the pathway and not in the end states was identified by the
simulations and subsequently verified by experiment.^{583}^{,}^{584}
The TMD method, which is particularly suited to model large-scale motions,
has also been used to determine the transition pathways for the
rigid-body-like domain motions of GroEL^{585}^{,}^{586} and
F_{1}-ATP synthase.^{124}

Two variants of the TMD method are implemented in CHARMM,
ζ – TMD and RP-TMD. In the ζ
– TMD method, the constraint is a function of both the initial
and final structures, rather than just the latter. The form of the
constraint is: *ζ*(*t*)
− *ζ*_{0}(*t*)
≤ *ζ _{tol}*, where

*ζ _{tol}* is a tolerance,

*ζ*

_{0}(

*t*) is the desired value of the restraint at time

*t*,

*C*is a constant, and

_{ζ}*R*

_{1}(

*t*) and

*R*

_{2}(

*t*) are the RMS deviations from the two target structures. This form of the TMD method is especially useful when the current structure is distant from either target or when the desired path does not involve a monotonic decrease in the RMSD from one target. The second variant is the restricted perturbation TMD method (RP-TMD),

^{553}which limits either the sum of the atomic perturbations or the maximal atomic perturbation at each step of the dynamics trajectory. It is designed to prevent large barrier crossings, so that the resulting paths can be closer to the actual PMF path than those obtained in the other TMD formulations.

A useful approach for simulations of biomolecules is to start with
targeted molecular dynamics or related methods with a large constraint that
provides a path between the end states, and to gradually reduce the
constraint so that the resulting paths approach the true path in the absence
of constraint.^{401}

#### The HQBMD Method

Half quadratic biased molecular dynamics (HQBMD) is a method that
forces a macromolecule to move between states characterized by the value of
a reaction coordinate, which changes with time along the trajectory. The
method is related to the minimum biasing technique introduced by Harvey and
Gabb^{587} and has been applied to
simulate stretch-induced protein unfolding,^{142}^{,}^{170} the denaturation
of a protein *in vacuo*^{588} and in implicit solvent,^{589} and the unbinding process for a hapten-antibody
complex.^{167} The perturbation is
a half-quadratic potential that depends on time through a reaction
coordinate ρ, which is a function of all or a subset of the
Cartesian coordinates of the system. The perturbation has the form

where*ρ _{a}*(

*t*) = max

_{0≤}

_{τ}_{≤}

*(*

_{t}ρ*τ*).

The minimum of the half quadratic perturbation “moves” as the reaction proceeds (i.e., as the reaction coordinate ρ increases). The reaction coordinate ρ is chosen in accord with the problem being studied. One such coordinate currently implemented in CHARMM is

This coordinate corresponds to the mean-square distance deviation
from a reference conformation (*F*) of a set of
*N* atoms that is considered sufficient to specify the
conformation of the object system being studied;
*r _{ij}* (

*t*)is the instantaneous distance between sites

*i*and

*j*, and ${r}_{ij}^{F}$ is the distance between the same pair of sites in the reference structure (

*F*).

If the coordinates of the reference conformation are all set to
zero, *ρ*(*t*) in Eq. (22) (i.e., the average squared
interparticle distance) is proportional to both the radius of gyration
(*R _{g}*) squared and the variance of the
position vectors.

^{†}

^{318}Several other reaction coordinates can be chosen within the HQBMD module. Among these are reaction coordinates which measure the deviation from experimentally measured “phi” values, a name introduced for the effects of mutations on the stability of protein folding transition states,

^{590}

^{591}

^{,}

^{592}and hydrogen exchange protection factors.

^{591}

^{,}

^{592}Both are assumed to be related to the number of native contacts or hydrogen bonds, or the deviation from measured NOEs and scalar dipolar couplings. Such biases have been used to sample slow native fluctuations and non-native states which are difficult to characterize by other means.

In an HQBMD calculation, the simulation is started at
*t*=0 with the value of
*ρ _{a}*(0) set equal to

*ρ*(0), the value of the reaction coordinate for the equilibrated starting configuration. If the reaction coordinate spontaneously increases in the simulation step from

*t*to

*t*+Δ

*t*, i.e.,

*ρ*(

*t*+Δ

*t*)>

*ρ*(

_{a}*t*), the external perturbation is zero and has no effect on the dynamics. In such a case,

*ρ*(

_{a}*t*) is updated and

*W*(

*r*,

*t*) is modified accordingly, i.e.,

*ρ*(

_{a}*t*) is set equal to

*ρ*(

*t*+Δ

*t*). If

*ρ*(

*t*)is smaller than

*ρ*the harmonic force acts on the system to prevent the reaction coordinate from decreasing significantly. The value of α determines the magnitude of the allowed backward fluctuation of the reaction coordinate and modulates the time scale of the reaction. The macroscopic state of the system is never changed since the perturbation is added to the Hamiltonian of the unperturbed system when it is numerically zero. Nevertheless, the perturbation affects the system working like a “ratchet and pawl” device

_{a,}^{593}that “selects” the sign of the spontaneous fluctuations biasing the trajectories toward the desired state. If the effective free energy surface is such that the motion of the reaction coordinate is diffusive in the absence of a barrier, the temperature of the system is not expected to change during the conformational transition. However, if there is a free energy barrier along the reaction path, the effect of the directed motion induced by the perturbation is to transform some of the kinetic energy associated with the reaction coordinate into potential energy. To avoid possible artifacts from temperature variation of this type, the simulations should be performed in the presence of a thermal bath using, e.g., Nosé-Hoover, or Langevin dynamics. The HQBMD method allows one to sample regions of the configurational space that are separated by either thermodynamic or kinetic (on a simulation time scale) barriers and determine low energy pathways. Other techniques, such as umbrella sampling, can be used to estimate the free energy profile along these pathways. For comparative purposes all the reaction coordinates available in the HQBMD module can also be manipulated by means of a harmonic potential whose minimum is displaced at constant velocity, in accord with a number of AFM experiments; this method is referred to as steered molecular dynamics. (SMD).

^{549}

^{,}

^{551}

^{,}

^{594}

^{142}

#### The AFM Method

The implementation of the AFM method in CHARMM has been motivated
by single-molecule experimental techniques, which offer a new perspective on
molecular properties.^{595}^{,}^{596} Such experimental techniques can
be simulated in CHARMM by, for example, using AFM SMD to mimic the effect of
a cantilever moving at constant speed, or by applying the biased MD approach
described above (AFM BMD) or a constant force (CF) to mimic a force-clamp
experiment. Alternatively, a force (constant or periodically varying in
time) can be applied to selected atoms in a specified direction
(*PULL* command). The PULL force vector can be specified
directly; alternatively, it can be specified indirectly in terms of an
electric field, **E**, which gives a force, q**E**, acting
on an atom with charge q.

#### Self-guided Stochastic Methods

To enhance searching efficiency and facilitate the study of
conformational changes in which the final state is not known, two
self-guided stochastic simulation methods are available in CHARMM:
momentum-enhanced hybrid Monte Carlo (MEHMC)^{416} and self-guided Langevin dynamics (SGLD).^{554} These approaches address several
problems^{416}^{,}^{597} inherent in the earlier self-guided molecular
dynamics (SGMD) algorithm that motivated them.^{568} They are much more robust than SGMD because
they balance the use of information about the average motion from previous
steps in the simulation with appropriate forms of dissipation.^{416} As a result, MEHMC and SGLD can
enhance the conformational search efficiency by accelerating the motion of
the system without significantly altering the ensemble of conformations
explored. Two parameters are used to control an MEHMC or SGLD simulation.
One is the local averaging time, which defines the slow motions that are to
be enhanced. The other is the guiding factor, which controls the degree of
enhancement. The application of these methods in peptide folding
simulations^{598} and in the exact
calculation of thermodynamic^{416} and
kinetic^{599} observables has shown
promising enhancements in conformational search efficiency.

### C) Potentials of Mean Force and Umbrella Sampling

Molecular dynamics simulations produce a series of states whose
equilibrium and kinetic properties can be estimated. However, sampling the
conformational changes involved in very slow processes by brute force
simulations may be impractical. One way to improve sampling is by the
introduction of systematic biases along one or more appropriately chosen
reaction coordinates that describe the progress of the conformational
change.^{556} Several of the general
restraints in CHARMM (see Section III F) can be used to introduce such a bias,
but CHARMM also provides the dedicated reaction coordinate facility RXNCOR and
the adaptive umbrella sampling module (ADUMB) to support biased simulations. The
RXNCOR module^{600} applies biasing energy
restraints along a chosen reaction coordinate. A general framework is provided
to define the reaction coordinates as a function of appropriately chosen degrees
of freedom of the molecular system. To analyze the biased simulations, the
potential of mean force (PMF) of the reaction coordinate and the value of the
reaction coordinate versus time can be printed out.^{408} The adaptive umbrella (ADUMB) sampling
module^{408} permits one to define
umbrella sampling coordinates, and to carry out a series of biased simulations,
in which the biases are adapted to obtain uniform sampling of the chosen
coordinates. Ensemble averages are obtained as a weighted average of properties
of the conformations from the biased simulations. The adaptive umbrella sampling
module implements the Weighted Histogram Analysis Method^{480}^{,}^{482}^{,}^{483}^{,}^{485}^{,}^{489} (see Section VI. A)
to determine weighting factors required to calculate the estimates for the
unbiased system. The ADUMB module of CHARMM supports multidimensional adaptive
umbrella sampling,^{408} and multicanonical
simulations.^{405}^{,}^{601} The former is used to obtain uniform sampling of
the space spanned by the chosen coordinates if several coordinates are of
interest. The latter uses the potential energy of the system as one of the
umbrella sampling coordinates, with the result that high and low energy
conformers are sampled with comparable probability. These biasing methods have
been shown to be efficient.^{488} Since the
effect of biases on the convergence of free energy values depends on the system
and the property of interest, selection of the best biases to speed convergence
has to be done on a case-by-case basis. Several biasing potentials have been
combined with umbrella sampling to determine the free energy surfaces associated
with conformational changes in biomolecules. For example, biasing potentials
applied to proteins and peptides have been based on the radius of gyration,^{298} native contact fraction (the fraction
of contacts relative to the native protein structure),^{299}^{,}^{602} RMS
deviation relative to reference conformations,^{603}^{,}^{604} the center-of-charge
along a proton wire,^{560} the position of
ions along the axis of membrane channels,^{33}^{,}^{91} and the pseudo-dihedral
angles controlling DNA base-flipping^{81}.
An adaptive umbrella sampling approach has also been implemented for studying
multidimensional reaction surfaces with combined QM/MM potentials.^{605}^{,}^{606} In addition, a cubic spline interpolation procedure has been
implemented for calculating an analytical bias potential, given the discrete PMF
values at a series of points along a given reaction coordinate.^{607} This procedure is particularly useful for
studying chemical reactions where the approximate barrier height and shape of
the PMF are known. It has been applied to a number of enzymatic reactions with
the RXNCOR module.^{258}^{,}^{259} These restraint functions are implemented in
CHARMM and have been integrated with many of the tools for the analysis of
conformational energetics and populations. Their application to protein and
peptide folding^{300}^{,}^{608} and to enzyme catalysis^{258}^{,}^{259} has
been reviewed.

#### Conformational Free-Energy Thermodynamic Integration

The Conformational Free Energy Thermodynamic Integration (CFTI)
approach is an extension of the well-known thermodynamic integration (TI)
method developed for free energy simulations.^{609} It is aimed at exploring multi-dimensional
free energy surfaces.^{610} The free
energy gradient with respect to a selected set of conformational coordinates
is calculated from a single simulation in which the coordinates are
subjected to holonomic constraints.^{610}^{–}^{612} This
method is closely related to the “Blue Moon”
calculation of the free energy along a reaction coordinate,^{613} and has recently been analyzed and
generalized to unconstrained simulations.^{614}

The free energy derivatives are determined by averaging the forces
acting on the constrained coordinates over a molecular dynamics simulation.
The generation of molecular dynamics trajectories with fixed values of
selected coordinates is performed using the holonomic constraint approach,
which is part of the TSM methods of Tobias and Brooks.^{357}^{,}^{615}
The basic TI formula for the derivative of the free energy
*G* with respect to a conformational coordinate
*ξ* is^{616}

where *U* is the system potential energy, the
angled brackets denote an average over a set of structures with
*ξ* fixed, and *J* is the
Jacobian of the transformation from Cartesian coordinates to a complete set
of generalized coordinates, *ξ* (i.e., such that
all conformations of the system may be represented by
*ξ*). A generalization of the TI formula to
several dimensions has also been developed.^{610}

Multidimensional free energy gradients are calculated from the
forces acting on chosen atoms and are evaluated at essentially no extra cost
compared to a standard molecular dynamics simulation. The method uses only
local information about the free energy surface, which may be sampled more
densely in regions of interest and less densely elsewhere. All the
“soft” degrees of freedom in the system, e.g., all
flexible dihedrals in a peptide, can be constrained to obtain both a
complete free energy gradient surface and fast convergence of thermodynamic
averages.^{612}^{,}^{617}

The free energy gradient makes possible different approaches to
exploring the molecular free energy surface. A series of calculations for a
range of coordinate values allows for the calculation of free energy
gradient maps, which can be integrated to yield free energy surfaces or free
energy profiles linking conformations of interest.^{612}^{,}^{617}
The free energy gradient can also be used to perform an optimization of the
free energy surface to locate free energy minima corresponding to stable
structures.^{611} Free energy
profiles connecting the stable states may then be generated, and the free
energy gradient integrated along them to yield conformational free energies
and transition state barriers on the molecular free energy surface.
Numerical second derivatives of the free energy with respect to the
coordinates of interest can be calculated, providing a measure of stiffness
or stability.^{611} The CFTI method has
been applied to the exploration of free energy surfaces of several peptide
and peptidomimetic systems: various helix types,^{612}β-sheets and collagen
triple-helices,^{612} model
β-peptides,^{617} and
the opioid peptide DPDPE in solution.^{618}

### D) Transition Path Sampling

The Transition Path Sampling (TPS) algorithm of Chandler and
co-workers^{561}^{,}^{562} uses Monte Carlo methods to sample the space of
whole dynamic trajectories. Such simulations not only permit determination of
the mechanisms of rare events but also the calculation of their rates. In other
words, time-dependent phenomena can be investigated using importance sampling
tools whose use has been traditionally limited to equilibrium properties.

The implementation of TPS in CHARMM^{563} can be activated through options for the reaction coordinate
definition (*RXNCor*) and molecular dynamics
(*DYNAmics*) commands. Two types of Monte Carlo moves are
provided. In “shooting” moves,^{561}^{,}^{562}^{,}^{619}^{,}^{620} a phase space point from an existing trajectory is selected, a
perturbation is made (typically to the velocities in a deterministic system and
to the random force in a stochastic one), and part or all of the trajectory is
regenerated by integrating from the perturbed point to one or both endpoints.
“Shifting” moves correspond to reptation in path space
and involve extending the trajectory at one end by integration and shortening
the trajectory at the other end. In both cases, new trajectories are accepted if
and only if they satisfy the constraints that define the path ensemble of
interest. Most often, these constraints are such that the endpoints of
trajectories must have order parameter values corresponding to the reactant and
product basins of an activated process, in which case the computational
advantage over straightforward MD derives from the fact that TPS eliminates the
waiting time for spontaneous fluctuations to the transition state region.
Because trial paths are generated from existing ones, the method can be
difficult to initiate in complex systems. To address this issue, a method for
annealing biased paths to unbiased ones was developed recently and implemented
in CHARMM.^{401}

The interpretation of TPS (and more generally, molecular dynamics)
simulations to delineate a mechanism requires identifying molecular features
specific to the transition state ensemble (defined here to be configurations
with equal likelihoods of committing to reactant and product basins in
additional simulations initiated with randomized momenta).^{409}^{,}^{621}
Because trial-and-error approaches to this task can require prohibitively large
investments of human and computer time, Ma and Dinner^{621} adapted automatic means for obtaining
quantitative structure-activity relationships (QSARs) to commitment probability
(*p*_{B}) prediction. The genetic neural network
(GNN) QSAR method of So and Karplus^{622}^{,}^{623} was used to
determine the functional dependence of *p*_{B} on sets of
up to four coordinates from a database of candidates, and to select the
combination that gave the best fit. Application of this method enabled the
identification of a collective solvent coordinate for the
C_{7eq}→α_{R} isomerization in the
alanine dipeptide.^{621} The TPS,^{562} bias annealing,^{401} and GNN^{621} methods were recently combined to elucidate a mechanism for DNA
damage recognition by the DNA repair protein O^{6}-alkylguanine DNA-alkyltransferase (AGT).^{624}

### E) Coarse-Grained Elastic Models

Coarse-grained modeling approaches, which are based on reduced
descriptions of molecules, are being increasingly utilized in studies of large
systems, such as macromolecules and complexes. They can provide useful
information at a fraction of the cost of the corresponding atomistic
calculations (see also Section IX D). One type of coarse-grained model, the
simplified elastic model, represents the protein by its Cα atoms and
the potential energy by harmonic energy terms corresponding to springs between
these atoms. Both “single-basin” and
“multi-basin” models have been developed. In the
single-basin models, fluctuations of the system in the neighborhood of a single
stable state, usually an unperturbed crystal structure, are of interest. The
first such model to be introduced is the so-called Elastic Network Model
(ENM).^{380} More elaborate treatments
are the Gaussian Network Model (GNM),^{625}
the Anisotropic Network Model (ANM),^{381}
and the recently introduced Generalized Anisotropic Network Model,^{626} which combines elements of the other
models. Since the potential is harmonic, a normal mode analysis yields exact
equilibrium properties, and the models have been used, for example, to give
estimates for relative B factors that appear to be in reasonable agreement with
experiment.^{627} As a component of the
vibrational analysis module VIBRAN in CHARMM, both the GNM and ANM calculations
can be invoked with the GANM option, for which a selection is available to
specify the atoms that are included in the coarse-grained network. An external
file unit is provided for reading in other network parameters. Based on an ENM
potential in the presence of external force perturbations, a linear
response-type approach involving non-equilibrium simulations has been used to
predict large conformational displacements in proteins.^{628} Another single-basin coarse-grained method
available in CHARMM is based on a Gō-like model.^{512} An extension of coarse-grained models replaces an
atomic description by force centers distributed in a uniform way inside an
electron density envelope for the system obtained from cryo-EM.^{629}^{–}^{631} An α-carbon-based model has also been used to study
the coupling between allosteric transitions of the *E. Coli*
chaperonin GroEL and the folding of a model substrate protein.^{632} The results support those obtained with the TMD
method and an all-atom representation for GroEL and the protein substrate.^{586}

For systems that undergo large conformational changes, an approximate
transition pathway or pathways between stable states can be determined through
the use of a “multibasin” extension of the elastic
network-type methods called the Plastic Network Model (PNM),^{633} which incorporates ideas from valence bond
theory.^{634}^{,}^{635} For a two-state system, the PNM method constructs
a 2 × 2 phenomenological Hamiltonian, where the diagonal elements
are the ENM energy of each conformer, and the off-diagonal elements are a
pre-defined mixing constant (or coupling parameter). The ground state energy of
the system is the lowest eigen-energy of the diagonalized PNM Hamiltonian. The
PNM module in CHARMM provides a simple yet smooth and continuous coarse-grained
potential, which can be used with the reaction path methods and non-equilibrium
dynamics methods described in the previous parts of Section VII for the study of
transition pathways between multiple protein conformations. The PNM method has
been used with the TREK module in CHARMM to obtain free energy pathways for the
open-to-closed conformational transition in adenylate kinase (ADK).^{633} Recently, coarse-grained simulations
combining PNM and TMD (Section VII B) have been performed to elucidate the
torque generating mechanism of F1-ATPase during its hydrolysis cycle.^{636} The PNM method can also be used as a
conformationally adaptive rigidification potential with an all-atom force field
in non-equilibrium all-atom simulations to prevent artifactual structural
deformations induced by the use of simulation times that are much shorter than
the actual transition times.

### F) Chemical Reactions and the Treatment of Nuclear Quantum Effects

The computational techniques described above, including reaction path
optimizations, umbrella sampling and free energy simulations as well as combined
QM/MM potential functions, provide the tools for modeling chemical reactions in
condensed phases and in enzymes. The study of reactions was set forth as an
important goal in the original CHARMM paper in 1983,^{22} and was realized a few years later in the study of
an S_{N}2 reaction in aqueous solution as the first application of a
QM/MM potential in an MD free energy simulation.^{248} Subsequent QM/MM studies, including detailed analyses of the
energetic contributions of specific residues, have provided further insights
into the roles of enzymes in lowering activation barriers. ^{251}^{,}^{258}^{,}^{637}^{,}^{638}

Transition state theory (TST) provides a fundamental approach for
describing the rates of reactions in the gas phase, in solution, and in
enzymes.^{259} The central quantity is
the free energy (potential of mean force) along the reaction coordinate. The
latter is expressed in terms of geometrical parameters, such as a dihedral angle
in peptide bond isomerization or the difference between the bond distances for
bonds being broken and formed in a proton transfer process.^{639} See Figure
7. The free energy can also be determined as a function of a collective
solvent reaction coordinate defined by the energy gap between the effective
diabatic potentials of the reactant and product states.^{640}^{,}^{641} The
associated transmission coefficient, which determines the fraction of the
trajectories that, having reached the transition state, go on to the product,
can be calculated from multiple trajectories, starting from the transition state
ensemble generated during the PMF simulations.^{555}^{,}^{557} This approach was
first applied to the enzyme triose phosphate isomerase,^{642} for which the calculated transmission coefficient
was found to be 0.4, indicating that the asymmetric stretch coordinate of the
transferring proton is a good choice. In a later study of the enzymatic reaction
catalyzed by haloalkane dehalogenase, in which the computed free energy barrier
was 11 kcal/mol lower in the enzyme than in the corresponding reaction in
aqueous solution, the transmission coefficient was found to be 0.53 in the
enzyme, *versus* 0.26 in solution.^{643} Applications to chemical reactions in solution
and in enzymes have been reviewed.^{258}^{,}^{259}^{,}^{639}^{,}^{644}
Transition path sampling (section VII D) provides a method that can be used to
study the reactions for cases where the transition state is not known. A recent
study with CHARMM of the hydride transfer reaction catalyzed by lactate
dehydrogenase found that residues aligned along the donor and acceptor atoms of
the hydride transfer reaction but distant from the active site are involved in
the reaction.^{645} These residues
participate in compression and relaxation motions that help to bring the donor
and acceptor atoms together so as to increase the tunneling probability.^{646}

**a)**Schematic diagram. Electron transfers are indicated in red, hydrogen bonds in green and enzyme

**...**

In contrast to most processes commonly studied with classical MD
simulations (see Section V B), reactions involving the motion of hydrogen atoms
and more generally reactions at low temperature have non-negligible quantum
dynamical effects and require the use of quantized vibrations and the inclusion
of tunneling corrections. Quantum dynamics is essential for treating kinetic
isotope effects (KIEs) of chemical reactions, which are of great interest
because the ratio of the rates between light and heavy isotopic reactions
provides the most direct experimental method for characterizing the transition
state of a chemical reaction. The CHARMMRATE module, which implements
ensemble-averaged variational transition state theory with multidimensional
tunneling (EA-VTST/MT), provides a procedure for introducing quantized nuclear
motion, given the classical PMF obtained from molecular dynamics simulations,
into the calculation of the rate constants of enzymatic reactions. The
EA-VTST/MT method combines the POLYRATE program, for computing rates of
gas-phase reactions^{647}^{–}^{649} with free energy simulation methods
employing combined QM/MM potentials in CHARMM.^{248}^{,}^{256} In the EA-VTST/MT
method, the classical PMF is first converted into a quasiclassical result, which
includes quantum effects for all bound vibrational coordinates (but not in the
reaction coordinate at the transition state), by making use of instantaneous
normal mode frequencies along the reaction coordinate. This is followed by
incorporating the contributions from nuclear tunneling in the reaction
coordinate at the transition state based on optimized tunneling paths averaged
over the transition state ensemble. In this procedure, the quantized system
evolves in a fixed protein and solvent field; this “frozen
bath” approximation is sufficient in many cases. Corrections to the
frozen bath approximation can be introduced in computing the tunneling
transmission coefficient by allowing for relaxation of the protein
environment.^{644}

Nuclear quantum effects can also be incorporated into enzyme kinetics
modeling through Feynman path integral simulations, employing both
classical^{533} and combined QM/MM
potential functions.^{650}^{,}^{651} For combined QM/MM potentials, a Fock matrix
updating procedure has been implemented into the QUB (Quantum Update in
Bisection sampling) module for centroid path integral simulations, such that
only the matrix elements for atoms that are treated with the path integral
approach need to be recomputed. A method has been developed that combines the
path integral approach with free energy simulations and umbrella sampling
(PI-FEP/UM). This method yields improved convergence in computed KIEs.^{650} As in the EA-VTST/MT method, the
classical PMF is first determined by umbrella sampling. Centroid path integral
simulations are then performed to obtain nuclear quantum contributions. Finally,
free energy perturbation simulations are carried out to change the atomic masses
to heavy ones by using the bisection sampling scheme to obtain KIEs.^{650} The PI-FEP/UM calculations include
both quantized vibrational free energies and tunneling. The method has been
applied to several chemical reactions in solution and in enzymes, and KIEs have
been determined for hydrogen and heavier elements (carbon and nitrogen).^{650}^{,}^{652}

## VIII. Analysis Techniques

The large amounts of data generated by molecular dynamics and Monte Carlo
simulations would be of limited utility without analysis facilities for deriving
pertinent information about the system from them. During a simulation, CHARMM can
intermittently write to the output file the values of all energy terms, as specified
by the user in the *DYNAmics* command, together with some basic
statistics (short-term and long-term averages, fluctuations and drifts). In
addition, CHARMM can write the energy values, binary coordinates, velocities, and
forces at user-specified intervals to files in a compact text format. All other
analysis of the simulation, with a few exceptions (e.g., free energy calculations
with PERT), is done via post-processing of the coordinate and/or velocity trajectory
files that are generated in the simulation. CHARMM has comprehensive and flexible
analysis facilities, which allow the efficient extraction of information from
individual structures or trajectories for the calculation of many system properties.
In this section, a description of the tools available for the analysis of static
structures is given first, followed by a description of tools for the extraction and
analysis of averaged and time-dependent information from trajectories. The section
ends with a discussion of modules for more specialized analyses. Together with the
general atom selection mechanism, these modules allow a very wide range of analysis
to be performed. Should the need to program some new analysis functionality arise,
there is a set of predefined hooks into various parts of CHARMM that allow
relatively straightforward modifications to be implemented without changes to other
parts of the program (see Section IX A).

The generation of the binary trajectory file during an MD simulation with
CHARMM is controlled by the *DYNAmics* command. The trajectory I/O
commands (*TRAJectory READ/WRITE/INQUire*) allow individual snapshots
to be extracted from a trajectory (*TRAJectory READ*), so that all
CHARMM analyses and processing functions for individual structures, as well as
external programs, can be applied to a trajectory by using the looping capability of
the CHARMM scripting language. This mode of analysis is thus very general, and
allows operations to be performed on subsets of atoms that may change between
snapshots on the basis, for example, of geometric criteria. New trajectories, with a
subset of atoms or with coordinates recentered around a solute or superposed onto a
reference structure, can also be constructed from one or several existing trajectory
files.

### A) Individual Structures

#### Structure

A large number of geometric characteristics of a structure can be
determined using the coordinate manipulation (CORMAN) and internal
coordinate (IC) modules (see Sections IX B and C). Some examples are
individual atom positions, distances between atoms, bond angles or torsion
angles, and properties involving a larger number of atoms, such as the
radius of gyration, least squares plane, accessible surface area, occupied
and empty volumes, ring puckering, or helix axis and dipole moment. There
are commands to find all distances, or just the minimum or maximum
distances, between two sets of atoms specified with the general selection
facility. Lists of hydrogen bonds and pairwise contacts between selected
sets of atoms, as well as histograms of atom densities (radially or along
the coordinate axes) can be easily generated. Coordinate differences, or
RMS-deviations with or without least-squares superposition, can be
calculated between two different coordinate sets (i.e., the main and
comparison sets). Protein secondary structure can be analyzed using the
definition of α- and β-structures proposed by Kabsch
and Sander.^{653}

#### Energetics

The potential energy of the whole system, a subset of the system,
or the interaction energy between two subsets (*INTEraction*
command) can be computed. Following an energy evaluation, the forces acting
on all atoms, a breakdown of the energy into contributions from each atom,
and the pressure are available. The user has control over which energy terms
to include in the analysis, and the values of the individual terms are
accessible at the CHARMM script level as variables.

### B) Trajectories

A CHARMM trajectory, which is stored in one or more files, can be
analyzed directly by several CHARMM commands and/or modules (e.g., *COOR,
IC*, *VIBRan*, *CORRel*,
*NMR*, *NOE*, *RDFSol*,
*MONItor*). Prior to analysis, CHARMM trajectories can be
processed by the *MERGe* command, for example, to reduce the
number of coordinate sets in the trajectory, to remove a set of atoms (this has
to be accompanied by the creation of a matching PSF), to orient the system with
respect to a reference structure, or to undo the effects of recentering of
molecules due to the use of PBC in the simulation.

#### Average properties

In the CORMAN module a number of average properties can be
calculated, including the average structure and RMS fluctuations around the
average; distance and contact matrices (*COOR DMAT*),^{299} which can be projected onto a
reference distance matrix for analysis of, e.g., native contacts; and the
distance fluctuation matrix and positional covariance matrix (*COOR
COVA*), which can be used to reveal regions that move
together.^{31}^{,}^{654}^{–}^{656} Other average quantities which can be calculated include
hydrogen bond average numbers and average lifetimes, histograms of hydrogen
bond lifetimes and lengths; density, charge or dipole histograms; and
internal coordinate averages. The pairwise RMSD can be calculated between
all frames in one or two trajectories (in the latter case, element a* _{ij}* is the RMSD between frames

*i*and

*j*in trajectories 1 and 2, respectively). The

*MONItor*command collects statistics on transitions between different minima for specified dihedral angles.

Techniques of conformational clustering are important tools for
analyzing the nature of the conformational space sampled during the course
of a molecular simulation. Clustering methods based on K-means or
hierarchical techniques^{298} can
provide estimates of the extent and nature of conformational basins sampled
during the simulation. A K-means clustering algorithm is implemented in
CHARMM.^{657} This algorithm
requires input of a time series for specific sets of conformational
variables — for example, sets of flexible torsion angles for a
molecular system throughout the course of an MD trajectory — and
a maximum radius for the Euclidian root-mean-square variation within any
cluster. The K-means clustering algorithm then uses a simple neural-network
scheme to iterate to a self-consistent set of clusters in the space of the
specified variables. The clustering methodology is integrated with the
CORREL and *MANTime* correlation function and time series
manipulation methodologies in CHARMM and thus permits the flexible
construction and combination of various time series for cluster
analysis.

Another clustering technique implemented in CHARMM involves the
projection of pairwise RMSDs between selected atoms in *N*
frames of a trajectory onto a 2-dimensional plane, such that the Cartesian
distances between the representative 2D points gives an approximation (least
squared fit) to the RMS deviations between the actual structures.^{171}^{,}^{658} Other clustering methods can easily be introduced into
CHARMM using the appropriate scripts. An example is given in Krivov
& Karplus.^{166}

#### Time-dependent properties

Time series of several pre-defined types of geometric and energetic variables can be extracted for user-selected sets of atoms in the correlation module (CORREL) in an efficient manner, since the trajectory is processed only once to extract all the data. These time series can then be further manipulated; for example a vector time series can be normalized or converted to spherical coordinates, an angle time series can be made continuous, or the angle formed by two vector time series can be computed at each time point. The time series can be read from or written to external files. Auto and cross correlation functions can be computed from the time series data, either directly or using a second order Legendre polynomial.

Examples of time-dependent properties that the CORREL module can extract from a trajectory for a selected set of atoms include fluctuations in vectors, components, and lengths defined by atom positions; energy and hydrogen bond properties; and the dipole moment for selected atoms or for a solvent shell of specified thickness. See Supplementary Materials for a more complete list.

#### NMR Analysis and NOE Distance Restraints

The NMR facility may be used to analyze a number of NMR-related
properties from a trajectory. Among the possible properties are those
related to dipole-dipole fluctuations that govern the relaxation rates in
solution NMR, such as T1, T2, NOE, ROE, and the Lipari-Szabo generalized
order parameter,^{324} as well as
non-isotropically averaged properties observed for oriented membranes and
liquid crystals, such as chemical shift anisotropy (CSA) and deuterium
quadrupolar splitting and dipolar coupling order parameters.^{31}^{,}^{137}^{,}^{659}^{,}^{660} Entropies associated with the generalized
order parameters are estimated using the simple diffusion-in-a-cone
model.^{661} A trajectory can be
analyzed as a whole, or in a series of windows of user specified duration,
with or without removal of overall translation/rotation individually for
each window; in the multi-window case, averages and standard deviations of
the extracted properties are reported. For trajectories created with a polar
hydrogen representation, the NMR facility can add missing hydrogens for use
in calculations involving proton NMR measurements. The NOE module, which is
primarily used to introduce distance restraints based on NOE data for
structure refinement,^{301} also allows
the analysis of how well a structure fits the restraints (see Section III
F).

#### Solvent Analysis

The aqueous environment of biological macromolecules plays an
essential role in their function. One of the advantages of MD simulations of
systems with explicit solvent is the ability to obtain a description at the
atomic level of the interactions of the solvent with the macromolecule.
Accordingly, CHARMM contains a suite of utilities for the analysis of
solvent properties. In addition to the general analysis modules (e.g.,
CORREL), there is a facility (*COOR ANALysis*) for direct
analysis of solvent properties. This makes possible the calculation of
solvent-solvent, solvent-solute or solute-solute pair correlation functions
with an excluded volume correction; translational and rotational diffusion,
in shells of user-specified thickness around a set of atoms; velocity
auto-correlation functions; number, charge or dipole density in 3D around a
set of atoms; hydration numbers; the distance dependent Kirkwood
g-factor;^{54} and the dipole moment
of a shell of solvent molecules. The pair correlation functions, as well as
the distance dependent Kirkwood g-factor, charge-dipole or dipole-dipole
orientational correlations functions between a set of reference atoms and
solvent molecules, can also be computed using the RDFSOL module,^{662}^{,}^{663} which is more efficient for large systems due to the use of
a spatial decomposition when computing inter-atomic distances. The RDFSOL
module is tightly integrated with the CRYSTAL/IMAGE functionality in CHARMM,
which is particularly useful for solvent-solvent analyses.

Another useful solvent analysis tool is the *COOR
HBONd* command, which uses the lists of hydrogen bond acceptors
and donors in the PSF; no explicit H-bonding terms are included in the
energy functions, but the acceptor/donor information simplifies the
analysis, which is purely geometric. With polar- or all-hydrogen
representations, it is advantageous to define the hydrogen bond in terms of
the hydrogen and acceptor atoms; the relevant hydrogen atoms in this case
are designated as donors. The *COOR HBONd* command takes two
user-defined atom selections, one for the hydrogen bond donors (hydrogens)
and one for the hydrogen bond acceptors, and determines from them all
hydrogen bonds meeting the specified distance and angular criteria and
calculates related properties. The calculated properties include the average
number of hydrogen bonds, their geometries and lifetimes, and their length
and lifetime histograms. The *COOR CONTact* variant of the
command performs a similar function, except that it disregards the hydrogen
bond donor/acceptor status of the atoms to be analyzed; it is useful, for
example, for hydrophobic contact analysis. For the case where a solvent
molecules moves in and out of contact with a given set of solute atoms
during the simulation, the “intermittent” residence
time (i.e., the time during which solvent molecules are present continuously
within a given distance of the solute atoms) can be obtained using
*COOR ANALysis*, as the relaxation time of the
auto-correlation of the function
*b _{k}*(

*t*);

*b*(

_{k}*t*)=1 if water molecule

*k*is within the specified volume at time

*t*, and 0 otherwise.

^{664}For solvent analysis on simulations with periodic boundary conditions, the commands described here take care of the periodicity for simple lattices (for

*COOR ANALysis*orthorhombic lattices; for

*COOR HBONd*orthorhombic, truncated octahedral, rhombic dodecahedral, and 2- or 3-D rhomboidal lattices). For solute-solvent analysis it can be advantageous to pre-process the trajectory such that the solute is placed in the center of each frame (

*MERGe RECEnter*). In this way, subsequent analyses of solvent properties in the vicinity of the solute can be performed without the need to account for the periodicity of the system, as would otherwise be necessary for cases in which part of the solute molecule is outside, or near the edge, of the primary box.

### C) Running statistics

The ESTATS facility calculates running averages and standard deviations (fluctuations) of the energies of the system and its components “on-the-fly” during a molecular dynamics simulation or any other calculation that serially calls the main energy routines. It collects the data at a user-specified step length for a user-specified interval during the calculation. The averages and fluctuations can be written to standard output or external files; they can also be assigned to CHARMM script variables.

## IX. Miscellaneous Tools and Applications

To use CHARMM functionality for production calculations such as MD simulations, free energy estimates, and reaction path sampling, the initial state of the system has to be set up properly. CHARMM has an extensive set of model-building facilities that includes a suite of tools for manipulating the Cartesian and internal coordinates of the system, and an automated procedure for constructing the topologies of large biopolymers (proteins, nucleic acids, and carbohydrates) from their constituent units. As part of its model-building capabilities, CHARMM also has a course-grained macromolecular docking facility called EMAP. For analyzing the results of calculations, the coordinate manipulation tools can be used in conjunction with the highly flexible scripting language (Section II C), the extensive set of analysis tools described in Section VIII, and novel analysis routines implemented directly in the CHARMM code by the user through designated “generic” subroutines. Although CHARMM data files can be used by external graphics programs for visualization of the initial system as well as structures resulting from production calculations, CHARMM has its own internal graphics facility, which has particular strengths. This section presents an overview of these CHARMM facilities, as well as some additional details related to CHARMM use.

### A) Some Details of CHARMM use

#### Generation of the Molecular System

Simulations of biomolecules and their environment in CHARMM make
use of a basic protocol that is required to establish the critical data
files. The reader should refer to the methodology introduced in Section II
A. CHARMM calculations are all initiated by specifying (and reading in) the
topology file and parameter file for the system of interest. As noted in
Section III, CHARMM provides topology and parameter files for proteins,
nucleic acids, lipids, carbohydrates, certain solvents and many other
relevant small molecules for a number of force fields, including those
currently under development. Once specified in this way, the system being
simulated is defined in terms of a set of “segments”
consisting of groups of atoms called “residues”.
Residues in CHARMM can represent a particular amino acid or nucleotide, a
solvent molecule, etc. A set of residues is grouped together and
“generated” using the *GENErate*
command into a particular CHARMM segment of an internal file structure
called the PSF; many biological macromolecules (proteins, nucleic acids) are
linear polymers, and the *GENErate* command uses rules, as
specified in the topology file, for covalently linking adjacent residues
into a linear chain. The designation PSF (protein structure file) was
originally used for proteins but now is a general term used for describing
the atomic connectivity, atom types and atomic charges for all of the
molecules studied in CHARMM. Several segments can be generated by repeated
application of the *GENErate* command, and these segments can
be modified using *PATChes* to provide disulfide bond
connectivity, alternate protonation states, modified terminal groups etc.
Generally, each individual protein (or nucleic acid) chain is denoted as a
separate segment; together with solvent, ligand or counter ion
“segments,” the chain segments make up the PSF. Once
the PSF is generated, the atomic coordinates may be read in or built using
the internal coordinate (*IC*) commands or the HBUILD routine
to place hydrogen atoms^{665} and
complete the structure. Examples of CHARMM input scripts can be found on the
CHARMM website (www.charmm.org) and in the “test”
directory of all CHARMM distribution packages.

#### Data Files

Most of the information needed to specify the molecular system
(RTF, parameters, coordinates) in CHARMM is stored in simple text files. The
only main data file used by CHARMM that is in a binary format is the
trajectory file, and CHARMM has built-in commands (*DYNA
FORMat/UNFOrmat*) to convert this to/from a text file for
interchange between computer systems with different binary representations.
External data (text) files, e.g. containing a list of dihedral angles to be
used with the internal coordinate manipulation commands for model building,
can be streamed directly into the CHARMM input file via the
*STREam* command. The CHARMM user specifies all file
locations, file names and file formats to be used – the program
makes no hidden assumptions about file locations or file-name
extensions.

#### Atom selections

The need to specify a subset of atoms, common to many operations in
CHARMM, is met by a general recursive atom selection facility. Atom sets can
be selected based on a number of properties including: atom number, IUPAC
name or chemical type; segment identifier; residue identifier, name or
number; distance from a point or other atom(s); connectivity (bonded to a
selected atom, all atoms belonging to the same residue or group); the
Cartesian coordinates; or any of several other properties contained in
internal CHARMM arrays (e.g., charge, mass, force). Ranges and wildcards are
allowed where appropriate, so that a single specification can encompass
multiple atoms. Selections can be combined using Boolean operators
*(.NOT.,.AND.,.OR.*), and they may also be given a name
for later reference with the *DEFIne* command. For example,
the command *DEFIne INTERESTING SELEct TYPE C*.AND. IRES
40:50 END* specifies the selection of all carbon atoms in
residues 40 through 50, inclusive, and assigns this subset of atoms to the
name “INTERESTING.”

#### Units

CHARMM uses a mixed set of units that are commonly used by
chemists. The distinct system of units for most commands is the
“AKMA” system, where distances are measured in
**A**ngstroms, energies in **k**cal/**m**ol,
masses in **A**tomic mass units and charge in units of electron
charge. Using this system, 20 AKMA time units is roughly 0.978 picoseconds.
For convenience, all input and output of the time is in picoseconds. Other
common units are also included; for example, vibrational frequencies are
provided in wavenumbers (cm^{−1}). The documentation
should be consulted for details on units.

#### Adding functionality

CHARMM has a mechanism for allowing users to implement their own special-purpose subroutines without altering other parts of the program. Six main “hooks” into CHARMM are provided as templates for such modifications. USERSB is an empty subroutine called by the USER command, intended as a general CHARMM subroutine template; USERE calculates an additional user-supplied energy term; USRSEL carries out a user-supplied atom selection; USERNM specifies a user-supplied vector for normal mode analysis; USRTIM specifies a user-supplied time series for use with the CORREL facility; USRACM is a user-supplied accumulation routine called at the end of each step of dynamics for direct statistical analysis, as an alternative to post-processing analysis. This interface mechanism is designed for short, one-time efforts. If a user-supplied subroutine is of general use, the routine should be rewritten to conform to CHARMM coding standards and incorporated into the program as an additional feature (see below).

### B) Coordinate manipulation and analysis tools

The coordinate manipulation (CORMAN) facility
(*COORdinate* command) primarily handles the manipulation and
analysis of structure and dynamics based on Cartesian coordinates. Seven
functions of this facility were described in the first CHARMM paper.^{22} The facility now comprises a much more
extensive set of command options. There are two primary sets of coordinates, the
main set and the comparison set, and the various coordinate manipulation
commands can be used with any subset of either set. The options also function
with image atoms defined by periodicity or symmetry. In addition, a second
comparison set can be used with the *SECOnd* option for all of
the commands (COMP2 keyword); this is useful when there are two comparison
structures, or when the main or first comparison coordinate set is being used
for another function. The coordinate arrays can be assigned the system
velocities (e.g., the comparison coordinates contain the velocities at the end
of a molecular dynamics simulation) or the system forces. A weighting array may
be employed as a general utility (4^{th}) array; mass weighting of the
coordinate arrays (often used when they are assigned the system velocities or
forces) is invoked with the *MASS* option. Examples of the
coordinate manipulation aspect of the *COOR* command are
*COOR ORIEnt RMS*, which performs a best-fit of one structure
with another (minimizes RMS difference) and *COOR AVERage*, which
generates an interpolated structure. An example of the coordinate analysis
aspect of the command is the *COOR COVAriance* option, which
calculates a covariance matrix from the system’s dynamic
fluctuations. See Supplementary Materials for a more complete list. For more
information and specific references for these command options, see the
“corman.doc” section of the CHARMM documentation.

### C) Internal coordinate tools

The internal coordinate (INTCOR) facility (*IC* command)
primarily deals with the inter-conversion between internal coordinates and
Cartesian coordinates and the analysis of structure and dynamics based on
internal coordinates. The original form of this facility has been previously
described.^{22} Together with the
*COOR* command and options, the *IC* command
options provide a complete non-graphical model-building facility. The facility
now contains two independent internal coordinate table structures, the main and
secondary IC tables. Each row of the tables has 10 components (4 atom
identifiers, 2 distance values, 2 angle values, one dihedral angle value, and a
logical flag indicating whether the 4 atoms represent a linear or branched
topology). Given the positions (Cartesian coordinates) of any three of the atoms
in a row, the position of the fourth atom can be defined in relative terms with
three values: a bond distance, a bond angle, and a dihedral angle specification.
For a chain of connected atoms (such as a protein), the information in the
internal coordinate tables allows the Cartesian coordinates of all the atoms of
the chain to be calculated from any 3 adjacent atoms with known positions. The
need for the calculation to be able to proceed in either direction along the
chain (e.g., from the N-terminal end to the C-terminal end of a polypeptide
chain, or *vice versa*) led to the symmetric structure of the
rows in the IC table (bond length--bond angle--dihedral angle--bond angle--bond
length). By necessity, the IC tables overspecify the structure. CHARMM employs
an improper dihedral angle internal coordinate to specify the geometry at branch
points, in which the central atom, from which the branching occurs, is the
3^{rd} atom in the entry. The *IC* command options
include *IC GENErate,* which generates an IC table for the
selected atoms; *IC BUILd,* which transforms the internal
coordinates to Cartesian coordinates; and *IC RANDom*, which
randomizes selected torsion angles. See Supplementary Materials for a more
complete list.

The internal coordinate tables are used by several other parts of
CHARMM. The MCMA (Monte-Carlo Minimization Annealing, Section V D) method uses
them extensively for generating move sets.^{143} The tables are also used for internal coordinate restraints,
which may be used to restrain the system to particular internal coordinate
values (*CONS IC* command). The vibrational analysis tools use
the IC tables to present internal derivatives for normal modes of vibration. The
IC tables are also used in adaptive umbrella sampling (Section VII C) and
conformational searching with the Z Module (Section V D) or GALGOR facilities.
The latter employs a genetic algorithm and is designed for docking small
flexible ligands and rigid proteins.^{666}
For more information on any of these commands and features and for specific
references, see “intcor.doc.”

### D) EMAP: molecular modeling with map objects

High-resolution electron microscopy (EM) is rapidly emerging as a
powerful method for obtaining low-resolution (10–30Å)
structures of macromolecular assemblies composed of hundreds of thousands or
millions of atoms.^{667} Docking of the
individual macromolecular components, whose structures are available at high
resolution, into the low-resolution EM maps of these assemblies can provide
insights into the functional architecture of the macromolecular complexes; an
example is given by the model for the actomyosin complex.^{668}
^{669} The EMAP facility in CHARMM is
designed to carry out this kind of macromolecular fitting in an efficient
way.

Conventional molecular modeling is performed at atomic resolution and
relies on X-ray and NMR experiments to provide structural information, but the
direct manipulation of very large biomolecular assemblies using atomic models is
very computationally demanding. To mitigate this problem, methods for
protein-protein docking, for example, often employ coarse-graining or other
simplifying approximations.^{670}^{–}^{672} The EMAP
facility uses map objects, which are essentially rigid representations of
macromolecules that lack a well-defined internal chemical structure, but are
composed, instead, of spatial distributions of certain properties, such as
electron density, charges, or van der Waals “core” (see
below).^{673} EMAP allows the user to
fit map objects corresponding to individual structural components (e.g.,
individual protein molecules) to larger, multi-component target map objects
(e.g., single-particle EM maps of the complexes). The movement of the map
objects is carried out through the use of data structures called rigid domains,
which contain the position vector and orientation matrix associated with the map
objects they represent. The fitting process for large macromolecules using these
reduced representions is computationally more efficient than it would be using
all-atom (conventional) models. Some macromolecular flexibility can be included
by “blurring” the spatial distributions of molecular
properties.

Several utilities are available to compare map objects and calculate
interactions between them. Four types of cross correlation functions are
implemented to examine the match between map objects: density correlation,
Laplacian correlation, core-weighted density correlation, and core-weighted
Laplacian correlation.^{673} The
“core” corresponds to the interior of the structure,
specifically that part of the structure whose density distributions are unlikely
to overlap with those of adjacent structures; the structure is mapped to a 3D
grid and a “core index,” which is a measure of the depth
of burial, is calculated for each gridpoint in the structure with an iterative
procedure that is based on the position of each gridpoint relative to the
surface, its Laplacian-filtered density, and the core index of neighboring
gridpoints. The core-weighted correlation function gives more accurate results
than direct density correlations for locating correct matches. A grid-threading
Monte Carlo (GTMC) algorithm has been implemented to search for the best fit of
map objects.^{673} The GTMC method combined
with the core-weighted density correlation function has been applied to study
the molecular architecture and mechanism of an icosahedral pyruvate
dehydrogenase complex.^{674}^{,}^{675} Also, map-map interactions determined
with the EMAP facility have been successfully applied in a protein-protein
binding study.^{676}

### E) CHARMM graphics

Computer visualization has become an integral part of interpreting and
understanding molecular data, and CHARMM provides several means of facilitating
this process. One approach to molecular visualization in CHARMM utilizes an X11
window and a sub-command parser (GRAPHX). X11 is a widely supported graphics
standard that is supplied on most Unix-based systems and is available as added
software for other machines. The X11 display is
“passive,” i.e., the graphics window changes in response
to typed commands (and not the mouse). This affords flexibility through the use
of a scripting language, so that, for example, repeated complex tasks can be
invoked via a single command (*STREam*). Commands are available
to change atom size and color, change bond thickness, add atom-based labels,
control which parts of the PSF are drawn, scale the image size, switch in and
out of side-by-side stereo mode, define clipping planes, enable depth cueing,
and perform other standard graphics operations. The immediate graphical feedback
can also serve as a learning aid for new users of the CHARMM program. Examples
of figures generated with the use of the CHARMM graphics facility appear in
Woodcock *et al.*^{571} The
GRAPHX rendering model has been kept simple, so that even a large molecular
system can be rendered quickly; stored trajectories for the system can be
rendered directly to the screen to produce “on-the-fly”
animations of an MD simulation. Details are given in the CHARMM
documentation.

The graphics facility has aspects that make it well suited for use with other parts of CHARMM. The first is its direct use of the internal data structures of CHARMM, including the PSF, without an I/O step. This can facilitate the design of CHARMM input scripts (by allowing immediate visualization of coordinate manipulations, for example), especially when image atom transformations are involved. The fact that bonds are drawn as they are defined by the PSF, and not by interatomic distance searches, is also useful for the diagnosis of model-building problems or in multi-scale modeling applications. A second feature of the facility is that, through the use of the general atom selection feature in CHARMM, the coloring of atoms can be based on many of the atom-related properties that are either stored or can be computed during a CHARMM run. For example, atoms can be colored according to their interaction energy or the forces from the last energy evaluation.

In addition to the CHARMM graphics facility, molecular visualization
based on CHARMM calculations can be performed with external graphics programs
such as VMD^{677} and Python/VPython,^{678}^{–}^{680} in conjunction with appropriately formatted
CHARMM output files. Standard file formats for CHARMM output files include (in
order of generality) Brookhaven PDB format, CARD coordinate file format (with or
without the PSF), or binary coordinate trajectory file format (with the PSF). In
addition to these standard file formats, the CHARMM graphics facility (which can
be compiled without X11) provides for several others, notably a PostScript
format (a close copy of the X11 screen drawing), and the output of molecular
coordinates as a scene description for POV-Ray, a widely used and freely
available ray-tracing program (www.povray.org). The primary use of
the ray-tracing export facility in CHARMM is to produce high-quality figures for
publications.^{681}^{–}^{685} Examples of the output of this
facility are shown in Figure 8. The image
files produced can be combined to make animations in the MPEG video format. The
use of the CHARMM graphics facility with these external graphics programs allows
the generation of publication-quality graphics in a reproducible, script-based
manner.

**...**

Accelrys has historically provided two graphics programs, Insight II
and QUANTA, which can be used for graphical representation of CHARMM results. An
automatic parameter estimation option for the CHARMm (commercial version) force
field developed by F.A. Momany and R. Rone is available in QUANTA.^{686} In recent years, progress has been
made in providing a closely integrated CHARMm interface in a product called
Discovery Studio (http://accelrys.com/products/discovery-studio/), which contains
a library of pre-configured CHARMm workflows created “behind the
scenes” using the workflow management program Pipeline Pilot. An
automated force field typing utility is available for use with all CHARMM/CHARMm
force fields from the Discovery Studio interface.

## X. Performance

Performance is one of the primary concerns in macromolecular simulations because longer simulation times (10 to 100 or more ns) are now often of interest for systems of increasing size. Many of the questions being addressed (e.g., free energy differences due to mutations) are more quantitative and require lengthy calculations to minimize the statistical error. To minimize the numerical error, double precision for floating point operations is used in much of CHARMM. The application of this standard, which is important for the reliability of the results, particularly in long simulations, carries with it a significant computational cost.

The performance of a program involves factors in at least three general categories: 1) the efficiency of the code running on a single processor, 2) the scalability of the code to many processors in parallel, and 3) the portability of the code to new computer hardware. This section describes the status of developments in the CHARMM program that concern these attributes and provides some relevant performance benchmarks.

### A) Scalar enhancements (*FASTer* options), semiautomatic code
expansion

A first step toward improving code performance involves
single-processor enhancements. Recent developments include improvements in the
optimized Ewald-direct calculation (real-space part of the Ewald sum) and the
periodic boundary list routines. In addition, in the CHARMM program there are
several ways for the user to carry out performance optimizations. They are
controlled by the choice of the compiler preprocessor keywords and use the
runtime *FASTer* and *LOOKup* commands. The
optimal preprocessor keywords and *FASTer* command options to use
in a given calculation depend not only on the problem (system size, type of
calculation), but also on the computer environment, since processor
architectures and compilers differ. While there are general guidelines, it is
generally up to the user to determine which compilation and runtime options
result in the most efficient code in a given case.

#### EXPAND preprocessor keyword

A number of preprocessor keywords are concerned with obtaining the best performance for individual systems. This subsection describes the use of the EXPAND and associated keywords. Other performance-related preprocessor keywords are discussed below; for a more complete discussion of the preprocessor, see Section XI A.

The “EXPAND” preprocessor keyword is designed specifically to enhance the performance of the CHARMM code through preprocessor-level optimizations that supplement the intrinsic optimization procedures of modern Fortran compilers. The “EXPAND” keyword instructs the compiler preprocessor to automatically expand the innermost loops in the selected routines. This is useful because there are many IF statements in the loops of the non-bonded interaction energy routines that are needed to support a variety of CHARMM methods; expansion moves these IF statements out of the loops. More recently this kind of expansion has been extended to whole subroutines. The procedure essentially introduces variables into the name of a subroutine that correspond to branches of its internal IF constructs, so that the subroutine is transformed into a “generic” parent subroutine. At compile time, the parent subroutine is automatically replaced by numerous daughter routines, each occurring within a larger IF block structure as specific instances of the variable parent subroutine, but with their internal IF statements removed. Hence, in this expansion procedure, a subroutine can be written and tested as a single routine with many internal constant IF tests, and then expanded into a large set of efficient routines that lack the IF tests. Expansion of subroutines with this technique can improve performance by 10–30%, depending on the code and the compiler.

#### FASTer command

The *FASTer* command controls the use of the fast
energy routines in CHARMM, which are essentially streamlined, optimized
versions of the slower, full-feature routines. Many internal IF statements,
as well as analysis and print options, second derivatives, and support for
several non-bonded energy options are absent from the
“fastest” versions of the fast routines. This
significantly speeds up their execution times, but places some restrictions
on their use. The options for the *FASTer* command are:
*OFF, DEFAult, GENEric, ON,* and *EXPAnd*.
The *OFF* option disables the faster routines entirely and
invokes the slow, full-feature energy routines. The *DEFAult*
option causes the use of the fast routines when possible. The
*GENEric* option invokes the
“generic” versions of the fast subroutines, which
support most CHARMM methods and options, including second derivatives. The
*ON* option invokes the faster but more limited fast
routines, and it is the default in CHARMM. The *EXPAnd*
option also invokes the faster routines, but with expansion as described
above, and it must be used in connection with the use of the EXPAND
preprocessor keyword during compilation. The *EXPAnd* option
generally gives the best performance, but as mentioned, some methods and
non-bonded energy options are not supported in connection with it. (See the
CHARMM documentation, under “energy.doc,” for
further details.) Using *FASTer ON* (without code expansion
or lookup tables) the single-processor performance on a standard 23,000-atom
joint AMBER-CHARMM (JAC) benchmark (DHFR with explicit solvent, periodic
boundary conditions and particle mesh Ewald on an IBM p-Series,
Power4+ CPUs) for CHARMM (161 ps/day of MD simulation time) is
similar to that of Amber 8 (PMEMD, 128 ps/day), NAMD (Version 2.5, 135
ps/day) and Amber 9 (PMEMD, 197 ps/day); see also below.

#### Lookup tables

In simulations of large systems with an explicit representation of
solvent (usually water), the calculation of the solvent-solvent non-bonded
interactions consumes a significant fraction (often on the order of
90%) of the total CPU time. The evaluation of each interatomic
interaction requires several floating-point operations, including division
and square root operations that are quite expensive. One approach to
increasing speed is to code the routines that handle these types of limited
but time-consuming operations in assembly language; however, assembly
language is difficult to modify and to port to different computer
architectures. Although it is used in GROMACS,^{687} it does not appear to significantly increase
the speed of the code over what can be achieved with lookup tables. Lookup
tables circumvent the need for many of the floating-point calculations and
hence achieve an important single-processor speedup. Tables are easy to set
up for any functional form using the same high-level programming language
that is used for the rest of the code (i.e., Fortran 95). However, if there
are many kinds of interactions, the tables can require so much memory that
the speed advantages of this approach are diminished because of inefficient
cache-memory use. In CHARMM, a table lookup routine has been implemented
with separate tables for solvent-solvent, solute-solvent and solute-solute
interactions (LOOKUP precompiler keyword; *LOOKup* command).
These lookup tables (one set containing the forces and, optionally, one set
containing the energies for each combination of atom types) are indexed
using the square of the interatomic distance, thus avoiding the square root.
The lookup routine can perform linear interpolation between table entries
for increased accuracy. This approach is memory-efficient for
solvent-solvent interactions due to the small number of atom types involved
(typically two for the common three-site water models), since only three
force tables (O-O, H-H, O-H) and possibly three energy tables are required.
The magnitude of the speedup due to the use of the lookup table depends both
on the size and composition of the molecular system as well as on the
computer system. The operation count in the inner loop is reduced by ~
50%, which is reflected in typical speedups of 1.5 –
2 compared to the standard fast energy routines in CHARMM, with the higher
number obtained for systems whose interactions are dominated by
solvent;^{688} for a system
consisting of 46000 TIP3P water molecules, without PBC, list update, or PME,
100 MD steps take 90 s with the lookup tables, compared to 190 s with
standard CHARMM or 129 s with GROMACS. In four spherical cutoff
benchmarks^{688} (systems ranging
from 14000 to 140000 atoms) the double precision lookup code is faster than
the assembler code in GROMACS, also in double precision. The table lookup
method has been implemented in CHARMM for use with atom-based spherical
cutoffs or the real space part of PME, with or without PBC, and it runs in
parallel. In NVE simulations using the lookup tables with linear
interpolation, energy has been shown to be well conserved.^{688}

### B) Parallel computation

Since many systems of biological interest, such as solvated protein
complexes and membranes, are large, and since long simulations of such systems
are often required, the performance of massively parallel molecular dynamics
calculations on supercomputers or clusters of hundreds or more PCs has become an
integral part of the field of computational biophysics. There are many facets to
parallel molecular dynamics methods, and the reader is referred to any of
several papers on the subject for a more thorough treatment^{689}^{–}^{693} The most important element in the different methods is the
choice of parallelization model, which determines the manner in which the
“work” of a calculation is distributed among the CPUs.
For molecular mechanics/dynamics calculations, there are at least 3 general
classes of models: 1) atom decomposition (replicated data), 2) force
decomposition, and 3) spatial or domain decomposition.

In atom decomposition, for a computer system with *p*
CPUs, each CPU is essentially assigned every *p*th pass through a
loop. For the bond energies, for example, a given CPU handles every
*p*th bond. For the non-bonded (van der Waals and
electrostatic) energies, which for large systems require the most computer time,
each CPU handles the interactions for every *p*th atom. One of
the advantages of this scheme is the load balance is very good —
i.e., the distribution of tasks among the CPUs is uniform. In CHARMM, the loss
of performance due to load balance in the atom decomposition model is typically
less than 5%, and the model performs well for up to
32–64 CPUs, particularly on shared-memory machines such as the IBM
SP2, the SGI Altix series, and the CRAY XT4. After recent enhancements, such as
the implementation of a column-FFT (COLFFT keyword) for PME calculations, which
reduces communication costs by partitioning the system into 1-D
“columns” and reorganizing the FFT calculation, the atom
decomposition model scales with a parallel efficiency of ~ 0.6 using 32 CPUs and
~ 0.3–0.4 using 64 CPUs on a Cray XT4 (dual-core AMD Opteron
processors) for MD simulations of systems of 50,000–400,000 atoms
with PBC and PME (see Table 2a). On this
machine, the scaling is similar for the largest and smallest systems. On a
distributed memory cluster (8Gb/s infiniband interconnects; see Table 2b) the scaling is approximately the same or
better at 32 CPUs, but has a somewhat wider range (~ 0.2–0.5) for 64
CPUs, with scaling for the larger systems that is poorer than on the shared
memory machine. This level of scaling is often considered adequate for
applications on many computer systems, and, for certain applications, even on
machines having a very large number of processors – e.g., for the
generation of many independent MD trajectories, (each of which is propagated on
a fraction of the CPUs). The disadvantage of the atom decompositon model is that
the communication costs are high for large numbers of CPUs, because all of the
data in the system must be updated on each CPU. This cost is significantly
reduced by the use of “recursive doubling” or
“hypercube” algorithms, ^{694} which change the number of necessary communication calls from
*P* to log_{2}*P.* Still, for large
systems and large numbers of CPUs, the time spent on communication dominates the
total run times (wall-clock times), especially on distributed-memory clusters of
CPUs (as illustrated above), and the scheme becomes inefficient. The atom
decomposition model, which was the first one to be implemented in CHARMM, is the
most thoroughly integrated with the various CHARMM functionalities. It is the
default, and is still widely used, particularly on many
“local” clusters, which have up to 100 or 200 CPUs that
are shared among multiple users. While most modern-day efforts to parallelize
biomolecular simulation programs focus on standard MD with either spherical
cutoffs or PME for long-range electrostatic interactions, in CHARMM, many of the
other modules/methods that are available also run well in parallel under the
atom decomposition model. The ones that are most commonly used are: QM/MM
methods, the EEF1 solvation model, the replica (molecular replication) methods,
the TREK reaction-path facility, the PERT free energy methods, targeted
molecular dynamics, the HQBM external perturbation facility, adaptive umbrella
sampling, soft core potentials, the Drude oscillator polarizable model, and the
VV2 operator-splitting velocity Verlet integrator. For the communication scheme,
CHARMM uses a customized version of MPI, called CMPI,^{695} which includes specialized operations optimized
for hypercube communication topologies and which can be useful more generally
for synchronous communication schemes in networks with higher latency.

**...**

In the force decomposition model,^{689}^{,}^{690} the
*N* × *N* matrix of non-bonded
interparticle interactions is partitioned into *p* pieces and the
set of *N* atoms is partitioned into *b* blocks,
where
*p*=*b*(*b*+1)/2.
Each of the *p* pieces is assigned to a different CPU. The
communication cost is reduced relative to that of the atom decomposition model,
because each CPU must only obtain the data of the CPUs assigned to the same
columns or rows of the interaction matrix, rather than all other CPUs. In
principle, the amount of data per CPU per communication call (the width of the
blocks in the interaction matrix) drops with increasing numbers of CPUs until
the limit of *b=N* is reached (one atom per CPU per
call). The disadvantage of the scheme is mainly that the number of necessary
communication calls still rises with the square root of the number of CPUs,
since the numbers of CPUs in each row and column increase in this way. A force
decomposition scheme has been partially implemented in CHARMM^{696} and further developments (particularly
improvements in load-balancing) are in progress.

Spatial (domain) decomposition schemes are essential for the effective
use of large shared-memory supercomputers and commodity clusters of thousands of
processors. The central idea in this approach is to partition the molecular
system into spatial regions and then to map or assign the CPUs to
non-overlapping subsets of these regions. The partitioning of space, the
assignment of CPUs, and the partitioning of the calculation, can be done in a
number of ways,^{692}^{,}^{693}^{,}^{697}^{–}^{700} but the
spatial decomposition methods all have in common the important attribute that
the data in each region is communicated only to nearby regions. This property
reduces the communication costs of spatial decomposition schemes relative to
those of the other methods for large numbers of CPUs. If the system is
partitioned into cubical regions whose side length exceeds the non-bonded cutoff
distance, the CPU assigned to a given cube must at most obtain data from the 26
surrounding cubes.^{698} In the direct
implementation of this method, each CPU is responsible for the calculation of
(about half of) the interactions involving the atoms in its assigned regions.
The disadvantages of the method include the fact that load balancing is not
straightforward, especially in irregularly shaped systems or ones with
inhomogeneous densities. Also, unless more sophisticated modifications are
implemented, the maximal number of regions to which CPUs can be assigned is the
total number of cubes in the system, or roughly
*V/r ^{3}*, where

*V*is the volume circumscribing the system and

*r*is the cube side length (e.g., non-bonded cutoff distance). To overcome the latter limitation, some programs, such as NAMD

^{693}use what is essentially a combination of force and spatial decomposition methods. A more recent development in spatial decomposition models is the introduction of so-called

*neutral territory methods*,

^{691}

^{,}

^{692}in which the spatial assignments of the CPUs are done in a manner similar to that described above, but in which each CPU is responsible for the interactions involving atoms that are often in regions outside its own. In the “midpoint” method, for example, a CPU is responsible for an interaction if the midpoint between the interacting atoms is within

*r/2*of its region.

^{692}Compared to conventional domain decomposition approaches, these methods reduce the “import volume” or amount of data each processor must communicate with its neighbors, and hence they can be more efficient for larger numbers (e.g., 1024) CPUs. Recently, a spatial decomposition model based on the BYCC list-builder

^{314}has been partially implemented in CHARMM. The scheme, which is under development, makes use of the fact that in the cubical partitioning approach described above, each CPU must obtain the data from only those CPUs assigned to regions within the “shell” of cubes surrounding its own region. It achieves good load-balancing by making adjustments to the spatial assignments of the CPUs during execution. Refinements, including support of periodic boundary conditions and other facilities in CHARMM, are currently underway. More detailed information on the parallelization of CHARMM, including a list of modules that run in parallel, may be obtained from the “parallel.doc” section of the CHARMM documentation.

### C) Portability

Because of the variety of available computer hardware and software
platforms, and because of continual changes and improvements in them over time,
it is important for a program to be portable. For example, in the past,
supercomputers were based on vector processors, and it was possible to compile
CHARMM executables that were optimized for several specific vector architectures
^{701} (using the CRAYVEC, PARVEC, and
VECTOR preprocessor keywords); these features were removed (with CHARMM version
31) because the architectures were no longer of interest (although the features
are available in older versions of the program, which are archived at Harvard).
Modern-day, high-performance computer systems are based on multi-processor
architectures (of up to 100,000 processors or more). A number of different
architectures exist, from so-called Beowulf clusters connected by widely
available off-the-shelf network communication equipment, to massively parallel
systems from major computer vendors (e.g., the CRAY TX4 or the IBM Blue Gene)
with much faster and more specialized connections that improve interprocessor
communication. CHARMM has been ported to nearly all these machines, in addition
to Macs and PCs, and most other currently available machines, processors,
operating systems, and compilers. It also runs on clusters of special-purpose
“MDGRAPE” molecular dynamics computers ^{702} and with certain accelerator hardware tools (e.g.
“MD Server” at NEC). Efforts to port the CHARMM code to
graphical CPUs (GPUs) are currently ongoing.

To make this portability possible, CHARMM development standards have
limited dependencies on vendor-specific programming language extensions. In
addition, CHARMM has a hierarchical set of communication routines that make it
easily adaptable to different parallel libraries.^{695} In most cases, no source code modifications are
required to optimize CHARMM’s parallelism for a new machine
architecture, e.g., any of the variety of multicore processors and systems that
have been introduced in recent years.^{703}
There are several levels of communication routines, the highest of which is
called from the standard energy routines and is independent of the specific
parallel architecture and machine type. The lowest level routines directly call
“send” and “receive” primitives
from the system libraries. The precompiler determines which routines are
included in a CHARMM compilation (as specified in the
“build/pref.dat” file). The use of the optimal routines
for a given system and machine type significantly improves the performance of
the code in some cases.

## XI. Program Management

CHARMM has over 550,000 lines of source code, is under continual evolution, and has to serve a large user community. These conditions create a set of administrative challenges. The contributions of a large group of developers from different parts of the world (see also Section XIII), often to overlapping parts of the code, must be systematized, integrated, organized, documented, and tested in a manner that allows the program to continue to grow in an error-free manner while preserving its many preexisting functions. In addition, the composition and distribution of the various versions of the program must be managed. This section describes some of the administrative and testing procedures that have been put in place, as well as the program’s documentation and official website (charmm.org). The program’s general organization, extent of usage, language history, and preprocessor function are also reviewed.

### A) Administration and Distribution of CHARMM

#### General Administration and Code Distribution

Through the collaborative efforts of many developers (see Table 3) and the CHARMM manager, the ongoing administration of the CHARMM program has evolved over more than 15 years into a stable procedure that makes possible the continued development of the program as a robust, versatile, and well-integrated molecular simulation package. There are two versions of the program: one that is available only to current CHARMM developers as a basis for code enhancements, and one that is released, also as source code, to a large and growing community of users. Two of the central functions of CHARMM administration are 1) deciding which new features are to be included in the release version of the program and 2) creating a new developmental version. Every six months, revised versions are distributed. New features and enhancements are incorporated into the developmental revision and bugs are fixed in the release revision. At present, December 30 and June 30 are the deadlines for submission of developments for the February 15 and August 15 distributions, respectively. Submissions normally include either new source files or modified versions of preexisting source files, or both, as well as the required documentation, testcases, and release notes (see also “developer.doc”). After collection of all the submitted code, interdependent modifications are merged, conflicts are resolved, and the integration is finally confirmed by checking all test cases. The CVS (Concurrent Versions System) repository is then updated to include the new developmental and release versions; all versions since c24 are archived in this repository; versions 22 and 23, which predated the use of CVS, are archived separately.

The CHARMM program is distributed as source code to individual academic research groups (see http://www.charmm.org/info/license.shtml for current information on how to obtain a license). For-profit companies should contact Accelrys Inc. (www.accelrys.com).

#### Organization of the Code

CHARMM distribution packages include the program source, the documentation, and the support data. The content of the current version, c34b1, is listed in Table 4. The “ChangeLog” files contain release notes of versions 23 through 34 (see www.charmm.org web site). The source code is located in the “source” directory. Each subdirectory of “source” contains the source files of a given module, with the notable exception of the “include” files, which are collected in the “source/fcm” directory. The preprocessor (prefx), which is required to install an executable, and a set of shell scripts that are useful for modifying the program code are found in the “tool” directory. The compilation of CHARMM requires the use of the Makefile corresponding to the given platform; this file is created in the “build” directory, where installation takes place, and where the subdirectory “UNX” contains Makefile templates for the machines supported by CHARMM. A C-shell script, “install.com,” drives the installation procedure. The current version of the force field parameter files is located in the “toppar” directory. Previous versions of these files can be found in the “toppar_history” subdirectory. The “doc” directory comprises the full set of documentation files. The “support” directory contains miscellaneous files that are either required for certain CHARMM functions (e.g., specialized parameter files) or useful as adjuncts (e.g., helpful input scripts). The subdirectory “support/aspara” contains implicit solvation parameter files and “support/bpot” contains stochastic boundary potential files (see also http://mmtsb.org/webservices/sbmdpotential.html). The “support/form” subdirectory contains forms for reporting user problems, bugs and development projects, and “support/htmldoc” contains facilities for converting info document files into html files. A few examples of image transformation files are included in the “support/imtran” subdirectory. The “support/MMFF” subdirectory contains a number of parameter files required for use of the Merck Molecular Force Field.

#### Language History

Because the development of the program that would eventually become
CHARMM began in the mid-1970’s (see Epilogue), before FORTRAN 77
was widely available, data structures and advanced flow control were
incorporated into the program design. The early versions of CHARMM were
written in FLECS, since it supported a variety of control statements such as
block-*if*, *unless*,
*when*-*else*,
*conditional*, *select*,
*repeat*, *while* and
*until*. To generate the FORTRAN source, the FLECS source
was processed by the FLECS compiler, *flexfort*. Data
structures for the connectivity (PSF), residue topology (RTF), force field
parameters (PARM), images, etc., were built in FORTRAN array common blocks.
A HEAP and STACK structure were also implemented using very long
one-dimensional arrays in the common block to enable internal program memory
management. HEAP can be expanded using the malloc function of the
‘C’ language. In 1993, the FLECS source was
converted into standard FORTRAN 77, and the parts of the code that were not
convertible were eliminated. Since version 24 (1994), all CHARMM source code
has been FORTRAN/Fortran-based except for a few routines involving
machine-specific operations, which are written in
‘C’. As of July 2005, new developments are required
to be written in Fortran 95 (and allowed extensions). The Fortran 77 portion
of the code is currently being converted to Fortran 95.

#### The Preprocessor and Its Function

CHARMM is implemented as a single, large cohesive program that is developed for use on a variety of hardware platforms with numerous compile options. The customization of the executable from a single source is accomplished by the use of a CHARMM-specific preprocessor, PREFX, which reads source files as input and produces FORTRAN files for subsequent compilation. PREFX was developed within the CHARMM community in 1989 and provides the following capabilities:

- Allows selective compile of code based on passed or derived flags.
- Supports a size directive allowing executables to support larger (or smaller) system sizes.
- Handles the inclusion of FORTRAN include files in a general manner.
- Allows semi-automatic code expansion and subroutine expansion (see Section X A).
- Allows comments on source lines following a “!” (a non-standard feature in F77).
- Handles the conversion to single precision.
- Checks non-comment lines for lengths exceeding 72 places (important for CHARMM versions preceding c35).
- Inserts keyword lists into selected FORTRAN arrays (or prints them on execution).
- Processes inline substitution of variable or subroutine names.

The determination of what modules/methods are included in a CHARMM
executable depends upon the keyword list in the
“build/*platform*/pref.dat” file.
The keywords in this list correspond to various methods and capabilities of
the CHARMM program (e.g., “GBMV” for the Generalized
Born/Molecular Volume module), and the preprocessor uses them to select the
parts of the code to be compiled. For convenience, the default pref.dat list
is extensive, so that “out-of-the-box” compilations
of CHARMM may result in executables containing features that are not
necessary for the user’s intended application, and this may in
some cases reduce speed. The user may improve the performance of the
executable by removing the preprocessor keywords corresponding to methods
that are not needed, and then recompiling. While the various methods in
CHARMM are designed to be modular, there do exist significant
interdependencies, so that the user is advised to carry out these
preprocessor keyword list modifications with care and to check the results
for consistency in test calculations.

#### Version Chronology

A chronology of the developmental and release versions of CHARMM
since the distribution of version 22 on January 1, 1991 is displayed in
Table 5. CHARMM version 19 was
finalized with the accompanying parameter set PARAM19 in 1989. Earlier
versions were distributed at varying time intervals. When the FLECS to
FORTRAN source code conversion was completed, the need for a version control
system was recognized, and the CVS system was introduced into the management
of CHARMM with version 24 in 1994. Since then, all files in the CHARMM
program have been subject to CVS control. As of c24a1, CHARMM program
distributions were divided into developmental and release versions.
Developmental versions carry newly introduced features and enhancements that
are in the testing phase, and release versions contain only stable and
tested modules. The current convention for version numbering began with
version 26. In
“c*nn*(*a/b*)*m*”,
c is for CHARMM, *nn* is the version number,
*a* (alpha) is for developmental, *b*
(beta) is for release, and *m* is the revision number. For
example, c32a1 is CHARMM 32 developmental revision 1 and c31b1 is CHARMM 31
release revision 1.

The last column of Table 5
lists new methods and features introduced into each developmental revision,
most of which have been described in this review. Interfaces have been
implemented for MOPAC QM/MM, GAMESS-US, GAMESS-UK, Q-Chem, CADPAC, POLYRATE,
and SCC-DFTB programs. Three independent free energy simulation modules were
implemented in version 22. As detailed in Section III D, a large number of
implicit solvation and implicit membrane models have been incorporated into
the energy code. They are: PBEQ, EEF1, ACE, SASA, GENBORN, GBMV, GBSW,
COSMO, SCPISM, FACTS, GB/IM, IMM1, and their variants. Parallelization of
CPU intensive code began as early as 1992. The current version supports a
variety of parallel platforms based on SOCKETS, PVM, MPI, LAMMPI and MPICH.
In 2003, CHARMM was modified to accommodate simulations of systems as large
as 10^{10} atoms. Segment, residue, atom type and residue ID names
were expanded to eight characters. The data file format was also expanded in
a manner that ensures backward compatibility. The changes were implemented
in c30a2x, finalized in c31a1, and released in c31b1.

### B) Testing

An essential requirement for efficient code development and porting to new machine and processor architectures is the availability of an effective suite of test cases. Test cases are continuously added to CHARMM to test newly implemented features across various platforms and machine types and also to provide users with example input files. In addition, old test cases are used to test newly added methods or features for compatibility with the rest of the code. This is done by verifying that the new CHARMM code generates the expected results for the old testcases. In the “test” directory, subdirectories corresponding to each CHARMM version contain test case input files for the features that were added in that version. The “test/data” subdirectory contains data files needed to run the test cases. In c34b1/test, there are 460 test case input files contained in 21 subdirectories.

Modifying the potential energy function requires extensive testing of
its derivatives. A basic test for the coding of potential energy functions is to
verify that the analytical forces **F*** _{i}* are consistent with the variation of the total potential energy

*E*(

**r**

_{1},

**r**

_{2},…,

**r**

*,…,*

_{i}**r**

*). In CHARMM, this can be tested explicitly using the*

_{N}*TEST FIRSt*command, which compares the analytical forces to the finite-difference estimates of the forces; for the latter, the x-component for the

*i*th atom is given as:

This test is clearly essential for the proper function of energy
minimization algorithms, the correct dynamical propagation in MD simulations,
and the accuracy and consistency of free energy difference calculations. Running
*TEST FIRSt*, preferably with several values of
Δx, is particularly important when new terms are added to the
potential energy (e.g., RMSD restraints, QM/MM interactions, PBEQ forces, etc.),
to ensure that the analytical energy gradient has been coded correctly. In
addition, *TEST FIRSt* allows the perturbation of the unit cell
within the CRYSTAL facility, as is required for the testing of the virial
computation. The analogous *TEST SECOnd* command is used to test
components of the Hessian computation against the finite differences of the
gradient. A variant of this code is used to calculate the Hessian by finite
differences when the analytic second derivatives are not available (*DIAG
FINIte* subcommand of *VIBRan*).

### C) CHARMM Distribution and Usage

The usage of the CHARMM program in the scientific community can be
measured in a number of ways. From 2002 to August 2007, a total of 714 academic
CHARMM licenses were issued through Harvard University. The number of active
CHARMm (commercial version) licenses issued by Accelrys as of early 2007 was
approximately 400; this included 20 government licenses, and the rest were about
evenly split between academic and commercial institutions. (In many cases, a
single institutional license issued by Accelrys represents multiple end-user
licenses.) According to the Science Citation Index, as of January 2009 the
original (1983) CHARMM paper had been cited approximately 7800 times and the two
other papers describing the CHARMM force field^{38}^{,}^{62} an additional 3000
times. The total number has grown steadily since the 1983 publication and now
averages ~700/year.

### D) The CHARMM Web Site and Documentation

#### Charmm.org

In 2003, the website http://www.charmm.org was created to serve the community of CHARMM users and developers. This website contains basic information, links to CHARMM developers’ homepages and resources, and the CHARMM forums. It is an active website and is expected to remain an important and up-to-date resource for CHARMM users and developers. The most heavily used areas of the website are the forums, where CHARMM-related discussions take place on a variety of topics; moderators volunteer their time to assist novice users and answer questions. There are currently more than 1100 registered users who have posted more than 7000 messages in 30 regular forums arranged in the following five major groups:

- User Discussion & Questions – General CHARMM usage forum
- CHARMM Interfaces – Discussions regarding the use of CHARMM with other programs
- CHARMM Community – News, events, bug reports, and suggestions
- CHARMM Information – General CHARMM information and searchable documentation
- Restricted Discussion – Communication among developers

#### CHARMM Documentation

The CHARMM documentation consists of a set of text files in the
“doc” subdirectory of all CHARMM distributions that
are also available as HTML files on the CHARMM website. Commands and
features of all methods are documented, with descriptions of syntax,
options, and usage. Examples of their use are also provided in many specific
cases, along with some theoretical background and implementation details.
The CHARMM Developer Guide (“developer.doc”)
provides basic programming information for CHARMM developers. It describes
the program’s organization, coding standards and rules,
documentation standards, developer tools, preprocessor function and usage,
compilation procedures, and code submission protocols. All of the
“.doc” text files are written in the info format and
can be read with the emacs editor. These info document files can also be
converted into HTML files for web browsers with the
“support/htmldoc/doc2html.com” script. In addition,
CHARMM lecture notes are available on the charmm.org website. They are
derived from a course that was first given at Harvard by a group of CHARMM
developers in 1982 and that has been updated and presented at a variety of
locations over the years, primarily at the NIH. Notes for roughly half the
lectures are available. Readers who wish to obtain practical experience with
CHARMM are referred to *A Guide to Biomolecular Simulations*
by O. Becker and M. Karplus,^{704}
which is based on a course in Molecular Biophysics that was given at Harvard
for several years.

## XII. Concluding Discussion

The primary purpose of the current paper has been to review the
developments in the CHARMM program that have taken place since the initial CHARMM
publication.^{22} In addition, the paper has
discussed some of the theory and principles upon which the method developments are
based and many of the biomolecular research problems to which they have been
applied. A review of this length, which represents a body of work spanning more than
25 years and encompassing contributions from hundreds of individual scientists,
would be impossible to summarize in a few concluding paragraphs. However, there are
several useful observations that can be made from an overview of the entire paper.
These concluding observations all center on the role of complexity in biomolecular
simulation. Their consideration is relevant not only to the development and use of
CHARMM, but also to biomolecular simulations more generally. It provides some
guidance for the investigator in applying CHARMM and other programs to problems of
interest involving macromolecular systems, and suggests a framework for thinking
about the problems, themselves.

The first set of observations relates to the utility of simple models. As computational speed continues to increase, the tendency in biomolecular simulations is to use ever more complex potential energy functions that describe systems in greater detail, presumably with higher accuracy. Early extended-atom models were followed by polar hydrogen models and then all-atom models. More recently, polarizable models have been introduced, and even quantum mechanical (“first-principle”) energy functions are used in some cases. For the representation of the aqueous environment around biomolecules, the development of implicit solvation models has followed a corresponding progression, which began with simple distance-dependent dielectric functions. Surface-area based models were then developed, and these have led in turn to more complicated representations of the solvation energy density. The latter are now being partly superseded by more accurate models, e.g., ones using an approximate or full Poisson-Boltzmann electrostatics treatment of the solvent. At the same time, there has been the development of explicit representations of aqueous solvent, from van der Waals spheres to more sophisticated multipoint charge and polarizable water models. As is demonstrated throughout the course of this paper and as is evident from the published literature, the more detailed or complex models are important. What is equally noteworthy, however, is that their existence does not necessarily displace the simpler models, which often continue to be used.

There are several reasons for this. The most obvious reason is that simple models tend to be faster or more efficient than complex ones. For a given set of computational resources, the simpler model in most cases offers the possibility of addressing a larger problem. An example is seen in MD simulations that are carried out with quantum mechanical potential energy functions, e.g., when molecular mechanics potentials are not adequate. For large systems, full-QM simulations are currently very limited in their utility for obtaining meaningful statistics (accessible simulation times are on the order of ps), because of the computational cost. A more useful approach, which is employed, for example, in studies of chemical reactions catalyzed by proteins, is based on QM/MM methods. It provides a suitable compromise: the parts of the system where the electronic structure changes of interest occur are treated with quantum mechanics and the rest of system is treated with (classical) molecular mechanics. At the other extreme of the scale of molecular simulations, “coarse-graining” methods have been used increasingly in recent years. They introduce simplifications that eliminate many or all of the individual atoms and thereby run counter to the trend of ever-increasing detail in simulation methodology. Coarse-graining enables simulations of very large systems, such as multimeric protein complexes, for which atomic level detail cannot be obtained experimentally, or for which obtaining similar results with an atomistic simulation requires much greater computational resources. An example is the use of an elastic network model to perform a normal mode calculation on the structure of a large multimeric protein complex obtained from cryo-electron microscopy data.

There are also less obvious reasons why simple models continue to be used. One is that the approximations that are inherent in the simpler model may be more appropriate, given the other aspects of a calculation. A good example of this involves the representation of solvent in structure prediction studies (e.g., MC studies or grid searches), in which there may be large displacements of the solute (e.g., protein) of interest at each step in the calculation. The use of explicit representations of solvent, i.e., individual water molecules, which generally provide the most detailed treatment of solvation effects, is, for practical purposes, often incompatible with such methods, because it can lead to bad solute-solvent contacts in a high fraction of the sampled solute conformations. By contrast, the use of any implicit solvation model—even the simplest surface-area based ones—circumvents this problem, because the relaxation of the aqueous environment around the solute is effectively instantaneous. Another reason for the use of simple models is that the data they generate are often more easily interpreted. For example, implicit solvation models introduce an effective free energy of solvation through a mean field approximation, which represents an average over the many degrees of freedom of the explicit solvent water molecules that would otherwise be present in the calculation. Another example is seen in the analysis of pairwise atomic electrostatic interactions, which is generally more straightforward with the use of a simple point-charge model than it is with a full QM potential energy function. Overall, the success of models at many different levels of complexity, as described throughout this paper, underscores the principle that use of the simplest model capturing the essential features of the system or process under study may optimize the investigator’s chance of obtaining and interpreting the data necessary to achieve useful insights.

A second set of observations in the paper concerns the complexity of methods and the systems to which they can be applied. Some of the methods described in the paper for application to large biomolecular systems were formulated for smaller systems. An example is a straightforward molecular dynamics simulation, which can be successfully “scaled” from small systems to large ones essentially by increasing the number of atoms. It might be tempting to hypothesize, from this type of observation, that if a computational method is well formulated and has been validated on small systems, it should be directly applicable to large systems as well. However, the majority of methods in CHARMM, many of which are discussed in this paper, have been specifically developed or modified for application to large, biologically relevant molecules—i.e., they differ significantly from related methods developed for small or homogeneous systems. For example, energy-based search facilities for small molecules did not have, nor did they require, the range of functionality possessed by the analogous facilities in CHARMM (e.g., the Monte Carlo or grid search modules). The study of large systems has also provided the main impetus for the development of more sophisticated path sampling techniques, solvation models, and free energy methods.

A prime example of the inadequacy of “simple scaling” can be found in the application of reaction path methods. If the simple methods for finding reaction pathways in small chemical reactions were directly applicable to conformational changes in proteins, most of the methods in Section VII would be unnecessary; but in fact, many reaction path methods that appear promising when tested on small systems (e.g., the alanine dipeptide) fail in proteins or other large systems. This is due in part to the fact that adequate sampling in large, inhomogeneous or asymmetric systems is qualitatively more difficult to achieve than in most small systems. The computational cost for a single step of a given sampling method will, at best, grow linearly with the number of atoms included, so that a given number of sampling steps is substantially more costly when performed for a whole protein, say, than a small drug-like molecule. Moreover, the size of the conformational space of a molecule grows exponentially with the number of degrees of freedom, so that far more steps are required to sample the same fraction of conformations for larger systems. In addition to the sampling problem, large conformational fluctuations (e.g., in protein folding), the effects of bulk solvation, and the contribution of entropic changes are much more important, in absolute energetic terms, in transition paths of large systems than in most small molecule reactions. A separate but related example is that small molecules have a much more uniform solvent exposure than large globular molecules, which have interior or buried regions. In the latter, the most accurate implicit solvation models must take into account both the direct interaction with the solvent and the dielectric effects, as a function of the solvent exposure of different regions of the molecule, which can also vary with conformation. Finally, even a “straightforward” classical MD simulation of a very large system such as a solvated multimeric protein will likely differ from that of a small or homogeneous system, if for no other reason than the calculation must be parallelized in order for meaningful statistics to be obtained in an acceptable length of (real) time. As illustrated by these examples, a principal reason why CHARMM has evolved into such a multifaceted program is that large, complex systems are qualitatively “different,” and their study requires its own set of methods.

A third set of observations involves the “simplicity” of the CHARMM program itself and the important role it has had in the program’s capacity to grow. This paper makes clear that one of the features that has been vital to the success of CHARMM as a tool for molecular biophysics research is its ability to incorporate new methods and functions. There are at least two major factors in its ability to accomplish this. First, although the program has evolved to become quite large and complicated, its global organizational structure remains relatively simple, in accord with Figure 1. One advantage of this simplicity is that the structure is more easily understood, modified, and expanded upon. As mentioned in the Introduction, CHARMM has been able to develop over the years without requiring large-scale reorganization. Although the code has of course undergone continual modifications and improvements, the basic structure dates almost to its inception three decades ago. The other factor, which is related, is that while CHARMM is to some extent modular, it lacks the complex structural coding hierarchies that characterize formally object-oriented programs. This exacts a certain cost, e.g., with regard to data encapsulation, but the benefit is transparency. Both of these types of organizational simplicity have “lowered the barrier”—not to imply that it is negligible—to the introduction of new methods, functions, and other modifications into the program over the years. In this sense, the complexity of CHARMM as it stands today, i.e., its diversity of function and its capacity to continue to expand, can be said to have arisen in large part from the simplicity of its design.

## XIII. Epilogue: The History and Future of CHARMM (Martin Karplus)

### A) Historical Perspective on CHARMM and its Evolution

It is of interest to document why and how a program such as CHARMM, which has involved the sustained efforts of a large group of people for many years, came into existence. Initially, the primary purpose of the program was to provide the group at Harvard with a vehicle for doing research. It is to the credit of the group of researchers who originally developed the program that much of their early work has served as a foundation for the subsequent growth of CHARMM into a rich research tool used by the global scientific community. In an academic setting, like that at Harvard, there is no permanent support staff to take on the task of program development in an organized fashion. One of the strengths of academic scientific research in America, in contrast to that in much of Europe, is the independence of assistant professors and the intellectual renewal that is brought about by graduate students and postdoctoral fellows, who then move on to their own positions. However, the lack of a permanent staff causes some difficulties. I realized that in my research group, the only way to preserve program developments by individuals working on a many different research projects with the common thread of a focus on microscopic and mesoscopic systems (e.g., from small molecules in solution to large proteins) was to have an all-encompassing program like CHARMM. The price of having a single program is, of course, the complexity that comes with size, but CHARMM is now a major research tool for the scientific community in large part because of this diversity of function. The modularity of the program has made it possible to adjust relatively easily to new demands and new possibilities. The CHARMM Development Project, which is administratively at Harvard University but involves all of the developers, is a continuing, collaborative effort to advance the CHARMM program as a state-of-the-art tool for macromolecular simulations. It is one of the great successes of the project that many persons have been able to work together to develop the program over a thirty-year period (see Table 3) and that the structure is in place to continue the developments into the foreseeable future.

CHARMM began with a program, now referred to as
“Pre-CHARMM”, which was developed by Bruce Gelin during
his years (1967–76) as a graduate student in the Chemistry
Department at Harvard University.^{705} He
had begun to do theoretical work in molecular quantum mechanics and started by
studying the application of the random phase approximation to two-electron
problems. He was collaborating with Neal Ostlund who was a postdoctoral fellow
at Harvard at the time. Soon, however, Gelin was drafted and, as a member of the
Military Police, ended up in a laboratory that was concerned with drug use (LSD,
etc.) in the U.S. Army. This aroused his interest in biology and when he
returned to Harvard to finish his degree, he wanted to change his area of
research to deal with biological problems. This fitted in well with my own
interests. Attila Szabo had just finished a statistical mechanical model of
hemoglobin cooperativity^{706} that was
based on crystallographic studies and their interpretation by Max Perutz. This
work raised a number of questions concerning the energetics of ligand binding in
hemoglobin and its coupling to protein structural changes involved in the
transition from the unliganded to the liganded state (the T to R transition).
The best approach to such a problem was to have available a way of calculating
the energy of the protein as a function of the atomic positions. The specific
objective of Gelin’s research was to introduce the effect of ligand
binding on the heme group as a perturbation (undoming of the heme) and to use
energy minimization to determine the response of the protein. To do such a
calculation on the available computers (an IBM 7090 at Columbia University was
our workhorse at the time) required considerable courage and a program with
which one could construct the energy function for a protein as large as a single
hemoglobin chain (about 145 amino acid residues in length). We did not have such
a program and Gelin began to develop software that would make it possible to
start out with a given amino acid sequence (e.g., that of the hemoglobin alpha
chain) and a set of coordinates (e.g., those obtained from the x-ray structure
of deoxyhemoglobin) and to use this information to calculate the energy of the
system and its derivatives as a function of the coordinates. Developing such a
program was a major task, but Gelin had just the right combination of abilities
to carry it out. The result was Pre-CHARMM (it did not have a name at that
time). Although not trivial to use, the program was applied to a variety of
problems, including Gelin’s pioneering study of aromatic ring flips
in the bovine pancreatic trypsin inhibitor,^{23} as well as the hemoglobin study already mentioned,^{707} and Dave Case’s analysis
on ligand escape after photodissociation in myoglobin.^{708} This work predated the MD simulation of
BPTI,^{4} which served as the basis for
the application of such simulation methods to a wide range of problems in
structural biology.^{11}^{–}^{13}

Gelin would have had a very difficult time constructing such a program
if there had not been prior work by other groups on protein energy calculations.
The two major inputs came from Schneior Lifson’s group at the
Weizmann Institute in Rehovot and Harold Scheraga’s group at Cornell
University. When I first decided to take up calculational approaches to biology,
I needed a place where I could work with a good library and a congenial group of
people who knew more about what I wanted to do than I did. I took a leave from
Harvard University in the fall semester of 1968 and went to join Shneior
Lifson’s group at the Weizmann Institute in Rehovot for six months.
There I met Arieh Warshel who came to Harvard as a postdoctoral fellow and
brought his CFF program.^{86} At Harvard, he
developed a program for what would now be called π-electron QM/MM
calculations for the ground and excited states of polyenes.^{244} His presence and the availability of the program
was an important resource for Gelin, who was also aware of Michael
Levitt’s pioneering protein energy calculations.^{709} For the choice of the energy function to
represent a protein and for many of the parameters used in the original extended
atom model (all H atoms were treated implicitly), the work of
Scheraga’s group, and in particular, the studies of Gibson and
Scheraga,^{710} were an invaluable
resource.

It soon became evident that for an ever-growing group of research uses,
it would be very important to have a program that was easier to use, adapt and
develop. This need led to the first version of the present CHARMM program, by
the authors of the 1983 paper.^{22} Each one
had a different background and different ideas about how to develop the best
program. As a result of many discussions, some rather heated, the first version
of the program was born. When we searched for a name for the program, we tried
to find something for which GANDALF could be an acronym; my daughter Reba was at
the time very much involved with the stories by Tolkien. This was unsuccessful,
so Bob Bruccoleri, one of the original CHARMM developers, came up with the name
HARMM (HArvard Macromolecular Mechanics), which might have served as a warning
for the uninitiated user but seemed inappropriate to me. The addition of
‘C’ for Chemistry led to the present name.

Because of the growing importance of macromolecular simulations in drug
design by pharmaceutical companies, an entrepreneurial lawyer, Jeff Wales and
his neighbor, Andy Ferrara, came to me in 1985 with the idea of establishing a
company that was based on distributing the CHARMM program to industry. This
seemed a good idea, particularly because the original concept was that Harvard
would make the CHARMM program available and the company, initially called
Polygen, would transform our academic tool into a commercial program. Only part
of the plan came to fruition: i.e., what has been distributed over the years by
the various incarnations of the company (Polygen, Molecular Simulations, Inc.,
and now Accelrys, Inc.) has been the Harvard program, with few changes other
than the introduction of license keys. However, the graphical programs QUANTA
and INSIGHT have been of considerable utility as front-ends to CHARMM,
particularly for inexperienced users. Recently, Accelrys has begun to contribute
to CHARMm and CHARMM in the same way as other
“developers.” An example is the GB-based implicit
solvation model for membranes.^{136} Also,
Accelrys has developed a number of scripts, particularly for side chain and loop
predictions (see www.accelrys.com for details).

One major concern I had in working out the arrangements with Polygen was that the academic distribution of CHARMM remain under Harvard’s (my) control. This was important to me because I wanted to keep the research aspect of CHARMM clear of interference by commercial objectives and to make certain that the program could be distributed at a reasonable price for academic and other (e.g., government) not-for-profit institutions. Toward the latter goal, the criterion I decided upon was that the price should be as low as possible, but high enough so that people would request the CHARMM program only if they had a genuine intention of using it, rather than merely wanting to add another program to their collection. To distinguish the academic and commercial versions, which I hoped would be significantly different, as mentioned above, the slightly different names—CHARMM (academic) and CHARMm (commercial)—were agreed upon.

At about this time, I met Rod Hubbard who was very impressed with the possibilities of macromolecular simulations and had the idea of developing a graphics program to illustrate the results. I invited him to come to Harvard, where he developed a program, called HYDRA for its seven modules or “heads”. It was an exciting project. Every day, Hubbard would show on the computer screen what he had developed overnight, and group members would try and use it, find the problems in the present version, and suggest new functionalities that would be helpful in research. In this way, mainly through Hubbard’s outstanding ability at graphical programming, a very useful graphical program was developed in record time. It is unfortunate that this paradigm is not followed more generally to avoid programs that please the developers but not the prospective users. The graphical interface program QUANTA, which was developed from HYDRA by Rod Hubbard and people at Polygen, has remained an important tool for users of CHARMM until now.

CHARMM has “evolved” for more than thirty years, and the community of CHARMM developers is now sufficiently dispersed that there is an annual meeting to discuss recent additions and developments. It begins with one or two days during which the developers present recent work. (There are thirty or more presentations.) This is followed by a half-day session during which the content of the next developmental version of CHARMM is discussed, and the parts of the existing developmental version that will be added to the release version are selected. Usually, new developmental and release versions are generated each year in August, with an update incorporating bug fixes released in February. The critical task of integrating the various developer contributions while resolving conflicts and ensuring standard coding practices is led by Youngdo Won, the CHARMM manager (see also Section XI), who assumes the ultimate responsibility for preparing the new versions.

One contribution of CHARMM, in addition to its function as a simulation
program, is that a number of other programs for macromolecular simulations are
direct, though not necessarily planned, descendants of CHARMM; for example, Paul
Weiner brought pre-CHARMM to Peter Kollman’s group and developed the
first version of AMBER from it. Similarly, Wilfred van Gunsteren was a postdoc
in my group, took pre-CHARMM with him and used it as a basis for GROMOS. These
programs, and many others that are less widely available but had their origins
in CHARMM, are now independently developed and each one has certain features
that make it unique. Finally, X-PLOR was a planned derivative of CHARMM. It
began while Axel Brünger was at Harvard, when the utility of
molecular dynamics in a simulated annealing mode for X-ray structure refinement
and NMR structure determination became clear.^{711} The great success of X-PLOR, and now CNS and CNX, has been due
in large part to Axel Brünger, their primary developer.

### B) Perspectives for the Future

There are two components to the future of CHARMM, one administrative and the other scientific. For both, the future looks bright. On the administrative side, a plan is in place for an executive committee (Bernard R. Brooks, Charles L. Brooks III, and Martin Karplus) to formally take charge of the program and its evolution at the appropriate time. To achieve this, an agreement between Harvard, as the copyright holder of the program, and two other institutions (NIH for Bernard Brooks and University of Michigan for Charles Brooks) has been codified. In this way, it is expected that the development and distribution of the CHARMM program will continue as it has in the past.

On the scientific side, it is appropriate to begin by quoting from the
Concluding Discussion of the original CHARMM paper:^{22}

“Our work focuses on the chemistry of condensed phases, with particular emphasis on the study of macromolecular systems found in biology. The program has been employed in projects ranging from the exploration of macromolecular solvation to protein-DNA interactions and many associated studies of constituent small-molecule properties. The very large size and lack of symmetry of these systems presents us with challenging computational requirements. The methods developed to deal with these demands have application in other areas of theoretical (e.g., fluid and polymer mechanics) and experimental (e.g., crystallography, structure refinement, NMR, and other spectroscopy interpretation) study. By simulating biological macromolecules, we hope to improve our understanding of their properties and of the forces acting within them. Such knowledge will in turn help to elucidate their function and the mechanisms involved in macromolecular structure and assembly, binding site recognition, and specificity. Enzymes are among the most efficient and versatile catalysts known. The chemical and physical understanding of proteins gained through simulation will be directly applicable to understanding these unique catalysts. Combined molecular orbital and empirical energy function calculations are planned to examine the detailed interaction of molecular mechanics with electronic structure. Nucleic acids and their transformations, which play an essential role in genetics, are being studied.”

Much of what was written twenty-five years ago is still valid today and most of the research listed as “in preparation” in the 1983 paper has been completed, published and incorporated into the CHARMM program. One important example is the development and widespread application of QM/MM methodology.

Given the great and continuing increase in computer power (the first petaflop machine has recently been reported), simulations will most likely evolve in several ways. As I describe below, the extensions to larger systems and longer simulation times is one direction. In addition, the fact that multiple simulations can be done as a routine matter makes possible the determination of statistical errors in the results. In reducing systematic errors, the use of more accurate and complex force fields (e.g., polarization, QM/MM) will likely play a role. Also, faster computations will aid in the development of improved models of biological phenomena, because shorter turnaround times for nanosecond simulations will permit the testing of more ideas. Moreover, the possibility of more accurate calculations, including free energy simulations, using generalized force fields should be instrumental in making computer-aided ligand design a reality.

An exciting recent development in molecular dynamics is that the
simulation time scales becoming available with modern computers (100 ns to
μs or even longer^{243}) are
making it possible to directly simulate biologically important events. This is
analogous, in an inverse sense, to the fact that while experiments on the ps
time scale were an important development, it was only when the time resolution
was extended to femtoseconds that the actual events involved in chemical
reactions could be observed.^{712}^{,}^{713} A striking recent result is that, by
running multiple simulations of 10 ns duration, the visualization of water
molecules migrating through a model of the aquaporin channel has been achieved
(Figure 9).^{714}^{,}^{715}
Another example is the observation in molecular dynamics simulations of the
formation of detergent micelles^{681}^{,}^{682} and phospholipid bilayers.^{716} That certain of these simulations were
done with other programs (e.g., GROMACS^{21}
and NAMD^{693}) shows how much the field
has matured. It is becoming ever more evident that cells are made up not of
isolated proteins, but of protein complexes, which have the essential functional
roles. The structures of such large multisubunit complexes are being determined
at an increasing rate. In all of them (they are almost all
“molecular machines”) conformational change is directly
involved in function. One example where such simulations have helped to
elucidate the mechanism, in this case the synthesis of ATP, is the use of free
energy and targeted molecular dynamics simulations of the enzyme ATP
synthase.^{124}^{,}^{717} Another complex that is now being studied by
molecular and normal mode dynamics is the ribosome, whose structure was
determined recently. The simulation of such large systems for the time required
to obtain meaningful results is now possible and broadens the role of simulation
programs like CHARMM in molecular biophysics.

**...**

The next step is the evolution of molecular dynamics simulations from
molecular and supramolecular systems to the cellular scale. Studies of the
formation of such assemblages will be more demanding. The simulation of more
complex cellular activities, such as synaptic transmission^{718} and the dismantling of the nuclear membrane on
cell division by the motor protein cytoplasmic dynein^{719} are two examples of interest. Much of this work
will build on the detailed knowledge of the structure and dynamics of the
channels, enzymes and other cellular components. Global simulations are likely
to be initiated with less detailed models. A recent example is provided by the
use of simplified normal mode calculations for the cowpea chlorotic mottle virus
as a way of interpreting low resolution (28 Å) cryoelectron
microscope data indicating the swelling of this virus at low pH,^{720}, or dynamics of processes involved in ribosomal
translocation.^{721} However, the
ultimate descriptions, which will necessarily include such details as the
possible effects of mechanical stress in a contracting neuromuscular synapse
upon its channels and other components, will require atomistic simulations.

Given the continuing improvements in molecular dynamics simulations,
another development will be their routine use by experimentalists as a tool,
like any other, for improving the interpretation and understanding of the data.
This has, of course, been true for many years as part of high-resolution
structure determinations^{301}^{,}^{711} and it is now beginning to occur in
the interpretation of the structural results by the scientists who obtained
them.^{722} When molecular dynamics is
a routine part of structural biology, it will become clearer what refinements
and extensions of the methodology are most needed to improve the results and to
perfect the constructive interplay between the simulations and experiment. The
exposure of limitations by such applications will, in turn, provide challenges
for the simulation experts, and catalyze new developments in the field. I hope
that before long such an interplay between experiments and simulations will be
an integral part of molecular biology, as it is now in chemistry.

## Acknowledgments

Contract/grant sponsors: NSF, NIH, DOE, Accelrys, CNRS, NHLBI

In a multiauthor paper, there is a legitimate concern that the people involved receive the credit that they deserve. In general, this is not possible without listing the contributions of each individual, as some journals are now requiring. For a paper of this length and complexity, however, any attempt at such specific attribution of credit is impractical. All the authors contributed to the writing and rewriting of significant portions of the text. The corresponding authors, designated by stars, were also involved in planning the manuscript and overseeing sections in the early stages of the writing. In both groups (starred and unstarred), the listing is alphabetical. One author, R.J. Petrella, needs to be mentioned individually because, in addition to writing a significant portion of the paper, he was instrumental in transforming a large number of separate write-ups into what is very nearly a unified whole.

The authors thank the referees for their helpful comments and David A. Case for serving as the editor of the paper. A number of people, other than those in the author list, have read and commented on the manuscript. They include Kwangho Nam, Arjan van der Vaart, Ioan Andricioaei, and Tom Darden.

In addition to all of the authors of the paper, many other scientists have
participated significantly in the development of CHARMM through the years. See Table 3; this list is included with all
distributions of the program (in “charmm_main.src”). Support
for the development of CHARMM, *per se*, and for researchers
concerned with CHARMM development, have come from many sources, including NSF, NIH,
DOE, Accelrys, and CNRS. It is not possible to list all of the grants individually,
but NIH grant RR023920 is acknowledged for its direct support of the ongoing CHARMM
conversion project. Part of the research in the B.R. Brooks group was supported by
the Intramural Research Program of the NIH, NHLBI.

## Footnotes

^{*}Method abbreviations, e.g., MD for molecular dynamics and MEP for minimum energy
path, and module names, e.g., PBEQ for the Poisson-Boltzmann module, as well as
preprocessor keywords (see Section XI B), are in ALLCAPS. CHARMM commands,
subcommands, or command options are in *ITALics,* with the first
four letters capitalized. (The parser in CHARMM uses only the first four letters
of a command; however, it is case-insensitive.) The term
“keyword” is reserved for preprocessor keywords, not
command options. File and directory names are enclosed in quotation marks, e.g.,
“build” directory. The “module”
designation refers to portions of CHARMM source code that form a modular
functional unit, not necessarily a Fortran module.

^{#}*k _{B}* is the Boltzmann constant;

*T*is the absolute temperature

^{†}Specifically, $\frac{1}{{N}^{2}}{\displaystyle \sum _{i=1}^{N}}{\displaystyle \sum _{j>i}^{N}}{{r}_{ij}}^{2}=\frac{1}{N}{\displaystyle \sum _{i=1}^{N}}{({\overrightarrow{x}}_{i}-\langle \overrightarrow{x}\rangle )}^{2}={{R}_{g}}^{2}=\langle {\overrightarrow{x}}^{2}\rangle -{\langle \overrightarrow{x}\rangle}^{2}=\mathit{Var}(\overrightarrow{x})$, where *$\stackrel{\u20d7}{x}$ _{i}* is the
position vector of atom

*i,*

*$\stackrel{\u20d7}{x}$*is the mean position vector (center of geometry), and

*$\stackrel{\u20d7}{x}$*

^{2}is the mean squared position vector. The double sum over squared interparticle distances is therefore expressible exactly as functionals of single sums.

## References

**41a.**Blondel A, Karplus M. J Comput Chem. 1996;17:1132–1141.

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