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J Chem Theory Comput. 2014 Jul 8;10(7):2677-2689. Epub 2014 May 1.

Improved Reweighting of Accelerated Molecular Dynamics Simulations for Free Energy Calculation.

Author information

1
Howard Hughes Medical Institute, Department of Pharmacology, Department of Chemistry and Biochemistry and San Diego Supercomputer Center, University of California at San Diego , La Jolla, California 92093, United States.
2
Schrodinger Inc., New York, New York 10036, United States.
3
Howard Hughes Medical Institute, Department of Pharmacology, Department of Chemistry and Biochemistry and San Diego Supercomputer Center, University of California at San Diego , La Jolla, California 92093, United States ; Howard Hughes Medical Institute, Department of Pharmacology, Department of Chemistry and Biochemistry and San Diego Supercomputer Center, University of California at San Diego , La Jolla, California 92093, United States.
4
Howard Hughes Medical Institute, Department of Pharmacology, Department of Chemistry and Biochemistry and San Diego Supercomputer Center, University of California at San Diego , La Jolla, California 92093, United States ; Howard Hughes Medical Institute, Department of Pharmacology, Department of Chemistry and Biochemistry and San Diego Supercomputer Center, University of California at San Diego , La Jolla, California 92093, United States ; Howard Hughes Medical Institute, Department of Pharmacology, Department of Chemistry and Biochemistry and San Diego Supercomputer Center, University of California at San Diego , La Jolla, California 92093, United States.

Abstract

Accelerated molecular dynamics (aMD) simulations greatly improve the efficiency of conventional molecular dynamics (cMD) for sampling biomolecular conformations, but they require proper reweighting for free energy calculation. In this work, we systematically compare the accuracy of different reweighting algorithms including the exponential average, Maclaurin series, and cumulant expansion on three model systems: alanine dipeptide, chignolin, and Trp-cage. Exponential average reweighting can recover the original free energy profiles easily only when the distribution of the boost potential is narrow (e.g., the range ≤20kBT) as found in dihedral-boost aMD simulation of alanine dipeptide. In dual-boost aMD simulations of the studied systems, exponential average generally leads to high energetic fluctuations, largely due to the fact that the Boltzmann reweighting factors are dominated by a very few high boost potential frames. In comparison, reweighting based on Maclaurin series expansion (equivalent to cumulant expansion on the first order) greatly suppresses the energetic noise but often gives incorrect energy minimum positions and significant errors at the energy barriers (∼2-3kBT). Finally, reweighting using cumulant expansion to the second order is able to recover the most accurate free energy profiles within statistical errors of ∼kBT, particularly when the distribution of the boost potential exhibits low anharmonicity (i.e., near-Gaussian distribution), and should be of wide applicability. A toolkit of Python scripts for aMD reweighting "PyReweighting" is distributed free of charge at http://mccammon.ucsd.edu/computing/amdReweighting/.

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