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Biophys J. Aug 2005; 89(2): 1237–1250.
Published online May 27, 2005. doi:  10.1529/biophysj.105.064154
PMCID: PMC1366608

Global Rigid Body Modeling of Macromolecular Complexes against Small-Angle Scattering Data

Abstract

New methods to automatically build models of macromolecular complexes from high-resolution structures or homology models of their subunits or domains against x-ray or neutron small-angle scattering data are presented. Depending on the complexity of the object, different approaches are employed for the global search of the optimum configuration of subunits fitting the experimental data. An exhaustive grid search is used for hetero- and homodimeric particles and for symmetric oligomers formed by identical subunits. For the assemblies or multidomain proteins containing more then one subunit/domain per asymmetric unit, heuristic algorithms based on simulated annealing are used. Fast computational algorithms based on spherical harmonics representation of scattering amplitudes are employed. The methods allow one to construct interconnected models without steric clashes, to account for the particle symmetry and to incorporate information from other methods, on distances between specific residues or nucleotides. For multidomain proteins, addition of missing linkers between the domains is possible. Simultaneous fitting of multiple scattering patterns from subcomplexes or deletion mutants is incorporated. The efficiency of the methods is illustrated by their application to complexes of different types in several simulated and practical examples. Limitations and possible ambiguity of rigid body modeling are discussed and simplified docking criteria are provided to rank multiple models. The methods described are implemented in publicly available computer programs running on major hardware platforms.

INTRODUCTION

The challenge of the postgenomic era, when large numbers of genome sequences have become available, has led to large-scale macromolecular structure determination projects using x-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy aiming at structure determination of individual proteins or their domains (1). Although this atomic information is extremely valuable, it is also limited, as increasing evidence indicates that proteins function in the context of the cell not as individual entities but in complex with other macromolecules. Consequently, the focus of modern structural genomics is rapidly shifting toward the study of macromolecular complexes (2,3). These macromolecular assemblies are difficult to study by high-resolution methods due to their large size, inherent structural flexibility, and often transient nature. Since in many cases the structures of individual components are available, models of complexes can be built by rigid body assembly of the components based on experimental information from lower resolution methods. Thus, cryo-electron microscopy (cryo-EM) reconstructions provide a framework for docking the high-resolution models into the shapes of macromolecular complexes. This approach leads in many cases to excellent results (2), but application of cryo-EM is usually limited to large macromolecular aggregates (starting from a few hundred kDa).

Small-angle scattering (SAS) (4,5) is a universal low-resolution method to study native particles in solution and to analyze structural changes in response to variations of external conditions. SAS needs monodisperse solutions of purified macromolecules, but, normally, does not require special sample preparation. Similarly to cryo-EM, the scattering of x rays and neutrons yields information about the overall shape of the macromolecule, and, thanks to the recent progress in the analysis methods, particle shapes can be reconstructed from the SAS data ab initio (611). Although the three-dimensional cryo-EM images typically provide more detailed shapes then the ab initio SAS reconstructions, the latter experiments (especially for x rays) and data analysis are much faster, and SAS is applicable to a broader range of conditions and sizes (from a few kDa to hundreds MDa).

SAS, also being a powerful tool for rigid body modeling, employs a different strategy from that of EM. In the latter case, the high-resolution models of the individual subunits are usually docked into the envelope of the complex obtained after three-dimensional image reconstruction. In contrast, the SAS modeling is data-driven, i.e., the spatial arrangement of the subunits is sought by a direct fitting of available experimental scattering data from the complex. Several approaches were proposed to speed up this computationally demanding search. In the method in Wall et al. (12), the subunits are first represented by triaxial ellipsoids to find an approximate arrangement followed by docking of the atomic models. In the constrained fit procedure (13,14), the high-resolution models are represented by bead assemblies and thousands of possible bead models are screened, also accounting for other results, from ultracentrifugation. Another set of modeling tools operates directly on atomic models using spherical harmonics to accurately compute the scattering from individual domains (15,16). Algorithms for rapid computation of the scattering from the complex (17) are coupled with three-dimensional visualization programs (18,19) for interactive fitting of the experimental data by manipulating the subunits on the computer display. A local automated refinement using an exhaustive search in the vicinity of the current configuration is also possible. In all the rigid body analysis approaches, the use of information from other methods is extremely valuable to build sound structural models. This can be, e.g., information about contacting residues from mutagenesis studies, distance constraints from Fourier transform infrared (20), data on surface complementarity and energy minimization (21), or residual dipolar coupling NMR data reducing the rotational degrees of freedom during the modeling (22).

We have developed a new set of methods for global rigid body modeling based on the fast spherical harmonics algorithms (1519). In the present program suite, either exhaustive or heuristic algorithms are employed for rigid body modeling of complex particles based on the SAS data. The methods cover different types of macromolecular complexes, allow one to account for the particle symmetry, to include information about the intersubunit contacts, to simultaneously fit multiple scattering patterns, and to add missing linkers or domains. The approaches and programs presented are applicable for the modeling of both x-ray and neutron scattering data. The efficiency of the methods is illustrated by simulated and practical examples, limitations and ambiguity of the rigid body modeling technique are discussed, and additional criteria for the choice of the best solution are presented.

THEORY

Rigid body modeling technique

Let us assume that a complex consists of K subunits with known structure. The scattering amplitude from each subunit in a reference position is denoted as C(k)(s), where s is the scattering vector in reciprocal space, s = sin(θ)/λ, 2θ is the scattering angle, and λ is the wavelength. The idea of modeling is to find the spatial arrangement of the subunits, scattering from which would best fit the experimental scattering from the entire complex. The subunits are moved and rotated as rigid bodies, which changes their scattering amplitudes. The scattering intensity I(s) of the entire complex is expressed as (23)

equation M1
(1)

where A(k)(s) denotes the scattering amplitude of the kth rigid body at the given position, left angle bracketright angle bracketΩ stands for the spherical average in reciprocal space, and Π(αkβkγk) is the rotational operator (24). The modified scattering amplitudes A(k)(s) of each body depend in the general case on six parameters, the vector of the shift rk, and the Euler rotation angles αk, βk, and γk. The use of spherical harmonics for the multipole expansion of the scattering amplitudes allows a convenient analytical representation of the scattering intensity in the form of

equation M2
(2)

Here, the complex functions equation M3 are the partial scattering amplitudes of the kth rigid body, which depend on its scattering amplitudes, equation M4 in reference position and orientation and on the six rotational and translational parameters. The reference amplitudes equation M5 can be calculated from the high-resolution structures using the programs CRYSOL (15) for x rays or CRYSON (16) for neutrons. The analytical relationship between equation M6 and equation M7 is described elsewhere (25).

Biological macromolecules and their complexes often contain equivalent subunits forming symmetric structures. The presence of symmetry can significantly reduce the number of non-zero terms in Eq. 2. Thus, for symmetric particles having point groups Pn and equation M8 it can be assumed without loss of generality that the n-fold axis coincides with the z axis, and that the twofold axis (in the case of equation M9 symmetry) coincides with the y axis, which leads to the specific selection rules for the spherical harmonics. In this case, summation in Eq. 2 runs only over symmetry-independent rigid bodies in the ensemble, and only over m equal to zero or multiples of n; and, moreover, in the case of equation M10 terms of order l0 with odd l, as well as all imaginary parts, vanish.

The goodness-of-fit provided by a given arrangement of bodies to the experimental data Iexp(s) is measured by the discrepancy

equation M11
(3)

where N is the number of experimental points, c is a scaling factor, and σ(sj) is the experimental error at the momentum transfer sj.

To construct physically sound models, the target function E = χ2 + ΣαiPi can be employed, where the penalty terms αiPi formulate the requirements of interconnectivity and absence of overlaps and also permit to incorporate additional information from other methods if available (e.g., interresidue distances). The penalty weights αi are selected to ensure the significance of the given penalty in each particular case and yield 10–50% contribution to the function at the end of the minimization.

The choice of the global minimization method depends on the number of adjustable parameters describing the complex, which in turn depends on the particle symmetry and on the available constraints. In the general case of an asymmetric complex, this number is equal to 6K–6 (the position and orientation of each rigid body is given by six spatial parameters, and the reduction by six is due to arbitrary orientation and position of the center of the ensemble). Fewer parameters are required when the symmetry is taken into account: in particular, 6K/n–2 for Pn symmetry and 3K/n for equation M12 symmetry.

Given the limited conformational space, for hetero- and homodimeric particles and for symmetric oligomers formed by identical subunits it should be possible to perform an exhaustive grid search of the best configuration in reasonable computing time. For macromolecular assemblies consisting of more than two distinct subunits, heuristic algorithms have to be applied for the global minimization. In the present article, four algorithms are described for global rigid body modeling depending on the type of the system (hetero- and homodimers, higher oligomers, multisubunit complexes, and multidomain proteins).

Fast modeling of homo- and heterodimers

A fast simplified algorithm can be designed for the modeling of globular homo- or heterodimeric structures whereby one monomer is rolled on the surface of the other. For this, the shapes of the two monomers are represented by angular envelope functions F(ω), where ω is the solid angle in real space. These envelopes can be generated by the programs CRYSOL (15) or CRYSON (16) on a quasiuniform angular grid (17) (Fig. 1 A) on the surface of a sphere. A sequence of Fibonacci numbers for the evaluation the values of polar angles defining the sampling directions (the greater the order of the Fibonacci grid, the greater the number of directions generated).

FIGURE 1
A quasiuniform grid of angular directions generated for 11th Fibonacci number (145 directions) (A) and the schemes of the two grid-search modeling approaches: DIMFOM modeling of heterodimers (B) and GLOBSYMM modeling of symmetric oligomers (C).

Starting from both monomers centered at the origin, the first monomer is rotated to bring the jth direction of Fibonacci grid ωj to the z axis, and the other is rotated to bring the ith direction of its Fibonacci grid ωi antiparallel to the z axis. In the case of homodimers with twofold symmetry axis, only the j = i case is taken to ensure the symmetric arrangement of monomers. Finally, the second monomer is shifted along the z axis by F(1)(ωj) + F(2)(ωi) + δ and rotated about this axis by an angle 0 < ψ < 2π with a discrete angular step (Fig. 1 B). The offset δ ~ 0.3 nm between envelopes ensures a reasonable contact between the surfaces of monomers and diminishes the probability of steric clashes. Using this algorithm, the second body is always shifted along the z axis, which significantly speeds up the computations (25).

The approach is implemented in the computer program DIMFOM, which makes a search over all ωj, ωi (with i = j in the case of symmetric homodimers) and discrete rotations of the second subunit, in order to find the arrangement best fitting the experimental data. Although this approach is limited by the low resolution of the envelope function and generates dimeric structures that contact each other approximately along the line connecting their centers, it is useful for rapid modeling of complexes consisting of globular domains.

A brute-force modeling of symmetric oligomers

Symmetric assemblies of identical monomers (homodimers, trimers, etc.) can be constructed by appropriate positioning of the monomer and generation of the symmetry mates. The entire structure is thus described by six parameters—shift of the monomer by r = (r, ψ, [var phi]) and rotation by α, β, and γ. Moreover, the scattering intensity computation using Eq. 2 is accelerated in the presence of symmetry thanks to the selection rules as described above. The limited number of parameters and rapid computation of the intensity make it possible to employ an exhaustive grid search procedure to minimize discrepancy in Eq. 3.

The general scheme of the procedure is displayed in Fig. 1 C. The spatial and angular grids for the search of the position and orientation of the monomer are generated as follows. The magnitude of the shift vector r is constrained by the experimental value of the radius of gyration of the entire oligomer equation M13 The average value of the shift from the origin is equation M14 where equation M15 is the radius of gyration of the monomer.3 Moreover, for the symmetry Pn (z is n-fold axis), it is sufficient to consider only displacements along the x axis. The global search of four (for Pn) or six (for equation M16) positional parameters is performed over the allowed range of r (by default from 0.95r0 to 1.05r0), whereby the angular parameters ψ, [var phi] of the shift are taken from a Fibonacci grid. The Euler angles triplets are also selected to yield discrete rotations about the axes matching the generated Fibonacci grid directions (the orders of the grids for rotations and translations may differ from each other).

To avoid the oligomeric structures with loose contacts between the monomers and those with steric clashes, the generated models of protein complexes are rapidly checked using the coordinates of Cα atoms. The two criteria (cross- and contact-criteria) are introduced as follows. In the given configuration of the oligomer a sphere is drawn with the radius of r = 0.76 nm around each Cα atom of the first monomer and all Cα atoms belonging to the symmetry mates are identified inside the sphere. For each such Cα–Cα pair, the distance d is computed. If d < 0.38 nm, the pair contributes to the overall cross-value as (1/d − 1/0.38). If 0.38 < d < 0.76, a contact value 1/d is assigned to the pair of monomers containing these Cαs. The threshold value of d = 0.38 nm, being the distance between two subsequent Cαs in a polypeptide chain, is also a good estimate of an average residue radius. Inspection of the high-resolution models of multisubunit proteins indicates that the residues, where the Cα atoms separated by more than two dimensions, are unlikely to contact each other. The two monomers are assumed to be in contact if their total contact value is larger than 2.5 nm−1 (which corresponds to at least one pair with d < 0.4 nm or to the existence of more than one pair). The overall cross-value is then normalized to the total number of monomers and the overall connectivity of the oligomer is computed from the contacts between individual monomers. Disconnected structures and those with the average cross-value exceeding the threshold of 50 nm−1 are discarded without calculating the scattering intensity, to speed up the minimization procedure; otherwise, the overall cross-value is added as a penalty Pcross to the target function. For nucleic acids, a similar criterion is computed using the coordinates of P atoms.

If the information about distances between specific residues in the oligomer is available (e.g., from mutagenesis studies or fluorescence labeling), it can be used as a further restraint. For each such pair of residues, the expected Cα–Cα distance dk0 is specified and a penalty term with a quasispring potential is computed as

equation M17
(4)

where dk is the actual distance in the given configuration. Again, coordinates of P atoms can be used to account for the distance restraints for nucleic acids.

The above exhaustive search algorithm of the quaternary structure of symmetric oligomers is implemented in a computer program GLOBSYMM. The target function to be minimized has the form

equation M18
(5)

where αcross and αcont are the weights of the corresponding penalties.

During the run the program keeps a list of 20 best solutions, which are grouped after the minimization is finished. All solutions within the group differ from the best model of this group with RMSD less than the threshold specified by the user (the default value is 20% of the experimental radius of gyration). The overall best model is saved onto a Protein Data Bank (PDB) file; the parameters of the representative solutions of each group are stored in the log-file and can be retrieved by GLOBSYMM to generate these models.

Quaternary structure determination of multisubunit complexes

The above exhaustive search methods are hardly applicable for complexes containing several symmetrically unrelated subunits. The conformational space to be explored would have been too large, leading to a prohibitively long computation time for the brute-force calculations. A feasible alternative to the exhaustive search methods is simulated annealing (SA) (26), a technique used for global minimization of multivariant functions in different fields, and, in particular, for ab initio small-angle x-ray scattering (SAXS) data analysis (10,11,27).

The main aim of SA is to perform random modifications of the system (i.e., in our case, of the current subunits arrangement) by always moving to configurations, which decrease the scoring function E but occasionally also to those increasing E. The probability of accepting the latter moves decreases in the course of the minimization (the system is cooled). At the beginning, the temperature is high and the changes are almost random, whereas at the end a configuration with (nearly) minimum E is reached. Further details of the SA protocol in its faster quenching version applied here are described elsewhere (28,29).

The minimization procedure starts from an arbitrary initial assembly of K subunits, e.g., from their arrangement in a tentative model of the complex or just from all subunits centered at the origin in their reference orientations. It is possible to fix selected subunits at their starting positions and orientations to preserve known substructures. The scattering amplitudes of the subunits equation M19 are computed and the scattering intensity of the complex is calculated using Eq. 2. A single modification of the assembly is done by rotation of a randomly selected subunit by an arbitrary angle [var phi] < [var phi]max about a rotation axis (selected from the Fibonacci grid) followed by a random shift r < rmax along an arbitrary direction. At each step only one subunit is moved/rotated, and it is sufficient to recompute only the amplitudes of this subunit in Eq. 2, which significantly speeds up the calculation of the scattering intensity.

In some cases, not only the experimental scattering pattern of the entire macromolecular complex but also those measured from its partial constructs (substructures) are available. Assuming the same arrangement of subunits in the substructure(s) and in the complex, all the data sets can be fitted simultaneously. The scattering curves of the substructures are computed from the appropriate subsets taken from the entire assembly. The use of multiple scattering data sets, similarly to the contrast variation technique in neutron scattering, permits one to increase the experimental information content and thus to obtain more reliable results.

The SA protocol is employed in the program SASREF to construct an interconnected ensemble of subunits without steric clashes, providing the possibility of fitting a single or multiple data set(s) by minimizing the target function:

equation M20
(6)

Here, the sum runs over the discrepancies to the available data sets. The penalty Pdis = ln (K/KG) ensures interconnectivity of the model, where KG is the number of subunits in the longest interconnected subset (graph) found in their current arrangement and Pcross requires the absence of overlaps between subunits. The contact criteria between subunits to find the longest interconnected graph and the crossover penalty Pcross and the contacts term Pcont have the same form as in the brute-force modeling of symmetric oligomers described in the previous section. The later term permits us to incorporate information about distances between residues or nucleotides similar to Eq. 4. Additionally, ranges of residues (nucleotides) can be specified that are expected to be in the contact, and the program selects the minimum distance between the two groups to verify the contact criterion.

For symmetric particles with K subunits in the asymmetric unit, appropriate symmetry mates are generated to build the model of the entire complex. In this case, only the symmetry-independent part is modified during SA, whereas the rest is generated by appropriate symmetry operations.

Rigid body modeling coupled with addition of missing fragments

Very often in practical applications the high-resolution models of the subunits are only partially available, and the structure(s) of some fragments remain unknown. This could be the case for multisubunit assemblies but also for multidomain proteins consisting of globular domains linked by flexible loops. The high-resolution structures or homology models may be available for the individual domains but usually not for the linkers. In this case, a combined rigid-body and ab initio modeling approach can be employed to determine the overall structure of the entire assembly against the x-ray scattering data. The idea is to simultaneously find optimal positions and orientations of the domains/subunits moved as rigid bodies and probable conformations of the flexible linkers attached to the appropriate terminal residues of the domains. These linkers are represented as interconnected chains composed of dummy residues (DR). In the DR representation, a loop or protein fragment with unknown structure is substituted by a flexible chain of interconnected residues with spacing 0.38 nm. Each DR has a form-factor equal to that of an average residue in water and the x-ray scattering amplitude from such a chain is readily computed as described in Svergun et al. (11) and Petoukhov et al. (27). Accounting for the scattering from the DR-represented portions, Eq. 2 takes the form of

equation M21
(7)

where equation M22 are the partial amplitudes of DRs comprising the linkers.

As in the above modeling of multisubunit complexes, SA is employed for global minimization. The initial DR linkers are planar zig-zaglike polylines connecting the appropriate residues between the domains. A single modification of the system is performed by a random rotation of the part of the structure between two randomly selected DRs about the axis connecting these DRs, or alternatively, a single DR is selected dividing the entire chain into two parts and the smaller part is rotated by a random angle about a random axis drawn through this DR.

Multiple scattering data sets from partial constructs (e.g., deletion mutants), if available, can be fitted simultaneously. The target function has the form

equation M23
(8)

Here, penalty Pcross requires absence of overlaps between the domains and the DR linkers; Pang and Pdih penalties to ensure proper distribution of bond and dihedral angles, respectively, in the flexible DR chains (27); and Pext is introduced to avoid a too-extended conformation of the DR loops by restraining their radii of gyration, as

equation M24
(9)

where equation M25 is the radius of gyration of the jth fragment consisting of Mj DRs, the value equation M26 being an Rg estimate of a globular protein containing Mj amino acids, and the sum runs over all the DR loops. The connectivity restraint, Pdis, used for the multisubunit complexes, is not required here—since the model is always interconnected, thanks to the DR linkers connecting the domains.

The above algorithm to reconstruct domain structure and missing fragments against single or multiple scattering data set(s) from partial constructs is implemented in the program BUNCH. The program is primarily oriented toward single chain proteins or symmetric assemblies containing one polypeptide chain per asymmetric part. BUNCH can, in principle, also be used for the modeling of macromolecular complexes consisting of several subunits, when not all the structures of the subunits are known. In this case, not only can missing loops within one single subunit be reconstructed, but also the shape(s) of the missing subunit(s) can be restored. As the linkers are usually heavily hydrated, a possibility is added in BUNCH to increase up to 50% the partial amplitudes of the DRs representing the missing loops, which allows one to take the bound water into account. It should, of course, be kept in mind that the configuration of the loops provided by BUNCH reflects an average conformation of (often flexible) loops, and can effectively serve as an indicator of the volume occupied by the loops, and not as a representation of their actual tertiary structure.

Scattering experiments, data processing, and analysis

The experimental SAXS data sets from the protein solutions used for testing the methods on practical examples were collected, following standard procedures on the X33 camera (3032) of the European Molecular Biology Laboratory on the storage ring DORIS III (DESY, Hamburg, Germany) except for the data from hemocyanin solutions collected on the D24 station at LURE (Orsay-Paris, France) (33). The sample preparation, data processing, and analysis are described in detail elsewhere (27,3437).

Computer programs and testing

The programs GLOBSYMM, DIMFOM, SASREF, and BUNCH run on IBM PC-compatible machines under Windows 9x/NT/2000/XP, Linux, and Mac OSX, as well as on major Unix platforms. The main features and possible applications of the four algorithms are summarized in Table 1. All the programs (except DIMFOM) are able to take into account particle symmetry by generating symmetry mates for the rigid bodies (and DR residues) in the asymmetric part (point groups P2–P6 and P222–P62 are currently supported). The programs were tested on simulated examples to adjust the parameters of the minimization procedures, in particular the weights of the penalty terms. The optimum parameters found are used in all the programs as default values. Both SA programs have two modes of operation: the user mode, using minimum input and the default values of the minimization parameters; and the expert mode, where these values may be modified.

TABLE 1
Comparison of algorithms for global rigid body modeling

RESULTS

Validation of the techniques against simulated data

The proposed methods were first tested against synthetic model examples. Theoretical scattering patterns were generated from known complexes taken from PDB (38), and these complexes were broken into subunits. The x-ray scattering amplitudes from the subunits were computed using CRYSOL and the structure of the complex was restored by fitting its scattering pattern by one of the above programs. Below we present a synthetic example of a protein-RNA complex to demonstrate that the methods are applicable not only to protein complexes.

The complex was constructed using crystallographic coordinates of two proximal monomers of glutamil-tRNA synthetase (GTS) complexed with tRNA (PDB entry 1g59; see Ref. 39). The entire crystallographic dimer (in Fig. 2 A, top row) has the molecular weight of 156 kDa and contains 468 amino acids and 75 bases per monomer. The two monomers are related by a twofold symmetry axis. First, the scattering curve of the dimeric complex was computed and randomized to yield a constant relative error of 3% in each data point (Fig. 2 B). Generation of other types of error distributions did not influence significantly the results of the modeling. The resulting curve was fitted with the P2 symmetry constraint using the structure of the GTS-tRNA monomer as a single rigid body. The reconstructions were made independently by three programs (DIMFOM, GLOBSYMM, and SASREF) all yielding good fits (not shown) to the simulated data with χ = 1.39, 1.12, and 0.93, respectively. The reconstructed models demonstrate the same arrangement of monomers as the correct dimer, with the RMSD between the atomic coordinates of the simulated complex and the rigid body models of ~0.2 nm. Typical parameters for the grid search methods used in the simulated and practical examples below were Fibonacci grid with 145 angular directions, the rotational sampling of 10°, and, if required, spatial step 0.1 nm. For the SA techniques, up to 10,000–20,000 function evaluation per temperature were made and the temperature was decreased with a factor 0.9 (maximum 100 temperature steps). The numbers of target function evaluations and CPU times required for running this test example on a 2.2-GHz Pentium PC machine are given in Table 1.

FIGURE 2
Validation of the rigid body modeling techniques on a simulated tRNA-GTS complex. (A) Models of the complex. (Top row) Initial model of the dimer (orange and gray) superimposed with the model reconstructed by SASREF using the distance restraint as described ...

To further explore the capabilities of SASREF, the GTS-tRNA monomer was split to protein and nucleic acid parts and the program was run to simultaneously fit the two simulated scattering curves of the entire complex and of the dimeric tRNA (the latter computed in the same way as described above for the complex) while adjusting the arrangement of the two rigid bodies in the asymmetric part (GTS monomer and tRNA monomer). At this more complicated level of modeling, ambiguous reconstructions were obtained with different configurations of RNA and protein parts, all yielding good fits to both scattering curves. An example of such ambiguity is presented in Fig. 2 A (middle row), displaying a configuration that fits the data of tRNA and of the complex with χ = 1.01 and 1.39, respectively (Fig. 2 B). The ambiguity can be resolved by using the distance constraints. In particular, proximity of U513 with Pro503 and of A573 with Gly121 permits us to obtain an unambiguous result within RMSD = 0.1 nm to the initial structure. The accuracy of the position and orientation of the monomer was characterized by the shift of its center-of-mass δr relative to the initial position, and by a rotation angle ω, respectively. The latter parameter was calculated by finding the rotation matrix bringing the monomer to its initial position, which can always by represented as a rotation by an angle ω around an axis. As the direction of the axis depends on the overall orientation of the entire complex, the magnitude of the rotation ω is the most specific parameter describing the change of the monomer orientation. The final model provides δr = 0.02 nm and ω = 2° (Fig. 2 A) and yields χ = 0.94 and 0.92 to the data sets (Fig. 2 B) of tRNA and of the complex, respectively. The parameters δr and ω are also listed below together with the RMSD, to characterize the quality of the obtained solutions.

The monomer of the GTS-tRNA complex was used to test the program BUNCH. It was assumed that GTS consists of two domains (Met1–Phe373 and Glu381–Ala468) connected by a seven-residues' linker of unknown structure and that the structures of the first domain in complex with tRNA and of the second domain are known. The task was to build the model of the entire monomeric complex which fits the simulated scattering curve from the monomeric GTS-tRNA (curve 3 in Fig. 2 B). BUNCH was employed to find the position and orientation of the second domain and the conformation of the linker, given the fixed tRNA and first domain part. Multiple reconstructions were performed, and the position of the second domain was in all cases correctly found, although not always with precise orientation. A typical model reconstructed by BUNCH (in Fig. 2 A) yields an overall RMSD = 0.58 nm to the correct GTS-tRNA monomer and χ = 0.95 to the simulated scattering curve (Fig. 2 B).

Rigid body modeling against experimental scattering data

After validation on simulated examples, the methods were employed to reconstruct quaternary structures of several macromolecular complexes with known and unknown crystal structures from the experimental data. We have selected objects from already published studies, most of which (except for the hemocyanin study) were user projects at the X33 beamline of the EMBL, Hamburg Outstation. For some of the projects, interactive rigid body modeling has already been performed earlier, and it was also interesting to compare the results of the new methods with the previously published data.

For all the cases, the x-ray scattering amplitudes from the individual subunits were computed by CRYSOL using default parameters (Van der Waals' excluded volume and the hydration layer 10% denser than the bulk water). This could in principle lead to an overestimate of the hydration layer contribution on the contact interfaces between the subunits, not accessible to the solvent. As the hydration layer effect as such is relatively small compared to the contribution of the macromolecule itself, these overestimates do not significantly influence the results. To verify this, CRYSOL was used to compute the scattering patterns from the obtained final models of the complexes, and the fits to the experimental data were practically the same as those given by the rigid body refinement methods.

Location of two small subdomains of the Escherichia coli F1 ATPase

The extrinsic F1 complex of the membrane-integrated ATP synthase of Escherichia coli (~380 kDa) contains five subunits in the stoichiometry α3β3γδepsilon (40,41). The experimental scattering pattern of E. coli F1 ATPase (in Fig. 3 A, right panel) is neatly fitted by the theoretical curve calculated from the crystallographic model of F1 ATPase from bovine heart mitochondria (PDB entry 1e79; see Ref. 42). For testing this crystal structure was represented as a heterodimer formed by the large (α3β3γ) and small (δepsilon) substructures as two monomers and the program DIMFOM was run to build the full complex by rolling the δepsilon-part on the surface of α3β3γ part. The reconstruction in Fig. 3 A (left panel) yields a good fit to the experimental data of the E. coli F1 ATPase with χ = 1.2 (Fig. 3 A, right panel). Building this model took ~30 min CPU time on a 2.2 GHz Pentium PC. Although the orientation of the δepsilon-part is different from that in the crystallographic model (ω = 180°, i.e., the δepsilon-subunit appears flipped around its long axis), the position of the substructure was found correctly (δr = 0.73 nm) and the overall shape of the protein was also retained (overall RMSD between the two models in Fig. 3 A is 0.62 nm). In the earlier model (34) constructed interactively the epsilon-subunit was also located at the bottom of F1 but at a somewhat different position which can be explained by the fact that the structure of the γ-subunit protruding to the bottom stalk was only partially available at that time.

FIGURE 3
Results of grid-search rigid body modeling of F1 ATPase using DIMFOM (A) and of tetrameric ZmPDC using GLOBSYMM (B). (Left panel) Structural models. The crystal structures and the results of the modeling are shown on the left and the right columns, respectively. ...

Quaternary structure of tetrameric pyruvate decarboxylase from Zymomonas mobilis

Pyruvate decarboxylase from the recombinant wild-type of Zymomonas mobilis (ZmPDC) (43) consists of four identical subunits with molecular mass of ~60 kDa, which form a symmetric (point group P222) homotetramer (PDB entry 1zpd; see Ref. 43). Solution scattering studies demonstrated that this enzyme has the same quaternary structure in the crystal and in solution (35), and the protein is an interesting test example for a brute-force modeling. The quaternary structure of the tetrameric enzyme was restored from the experimental scattering pattern in Fig. 3 B (right panel) by GLOBSYMM starting from an arbitrarily positioned crystallographic monomer and assuming a P222 symmetry. The best model reconstructed in several runs of the program with different order of Fibonacci grids fits the experimental data with χ = 0.96 (Fig. 3 B, right panel) and yields the RMSD of ~0.6 nm from the atomic coordinates of crystal structure (δr = 0.45 nm and ω = 10° in terms of the monomer position and orientation). A typical GLOBSYMM run (145 Fibonacci directions for both rotation and positioning) required ~15 min on a 2.2-GHz PC. The comparison of the reconstructed model with the crystallographic tetramer is given in Fig. 3 B (left panel). This example was also used to test the additional criteria for ranking of the solutions (see below).

Arrangement of functional units in proteolytic fragments of the Rapana venosa hemocyanin (Hc)

Hc from Rapana venosa is a giant oxygen-binding protein (with MM of a few MDa) built from the functional units of MM = 50 KDa. The two experimental scattering curves (Fig. 4 A, left panel) from 100- and 150-kDa proteolytic fragments of Rapana Hc (36) containing two and three functional units, respectively, were employed for rigid body modeling of these fragments. Assuming that the two first functional units have the same mutual arrangement in 100 and 150 kDa fragments, both scattering data sets were fitted simultaneously using the crystal structure of the functional unit of Octopus Hc (PDB entry 1js8; see Ref. 44), which has 50% sequence identity (66% similarity) with the one from Rapana Hc. The program SASREF performed restrained rigid body modeling of the Hc proteolytic fragments against the two data sets requiring a single chain connectivity of the entire model. To fulfill the latter requirement, the distance between the C- and N-termini of the adjacent functional units was restrained not to exceed 1 nm. The resulting model shown in Fig. 4 A (right panel) demonstrates linear arrangement of the functional units in agreement with the results of the earlier study of low-resolution structure of Rapana Hc proteolytic fragments (36). The entire model and the subset of its first two units yield the fits with χ = 3.3 and 1.2 to the scattering data from 150- and 100-kDa fragments, respectively (Fig. 4 A, left panel). Interestingly, the small systematic deviations between the calculated and experimental data of a 150-kDa construct (curve 1) were also observed in the previous study (36) and they may indicate slight polydispersity or flexibility of the 150-KDa proteolytic fragments. A typical SASREF run required <4 h on a 2.2-GHz PC.

FIGURE 4
Results of rigid body modeling of Hc (A), BTK (B), and GST-DHFR (C) using simulated annealing. (Left panel) X-ray scattering patterns. The experimental data in AC are displayed as symbols, and the fits from the reconstructed models are shown ...

Domain structure of Bruton tyrosine kinase (BTK)

BTK consists of four rigid domains with known high-resolution structures (PH, SH3, SH2, and kinase domain) connected by linkers of unknown conformations. The SAXS patterns of the full-length BTK and its deletion mutants containing PH–SH3–SH2 and SH3–SH2 domains are presented in Fig. 4 B (left panel). In the earlier study (37), conformation of the full length BTK in solution was determined by subsequent fitting of the scattering data. The rigid body modeling started from the smaller construct (SH3–SH2) followed by the addition of other two domains one by one. The missing linkers were then added to the fixed arrangement of the four domains. In the present article, BTK conformation was reconstructed by simultaneous fitting of the three data sets from the two deletion mutants and from the full-length protein by the program BUNCH. A typical result presented in Fig. 4 B (right panel) displays an extended conformation of the full-length protein with weak interdomain interactions. This finding correlates with the previous results also demonstrating nearly linear domains arrangement. All the scattering data were neatly fitted as shown in Fig. 4 B (left panel), where SH3–SH2, PH–SH3–SH2 portions and the full-length model yield χ-values of 0.70, 0.48, and 0.91 to the appropriate experimental curves. A typical BUNCH run required ~12 h on a 2.2-GHz PC.

Structural characterization of GST-DHFR fusion protein

The GST-DHFR fusion protein consists of Schistosoma japonicum glutathione S-transferase (GST, MM = 26 kDa) and E. coli dihydrofolate reductase (DHFR, MM = 17 kDa) connected by a 10-residues' linker. It was shown (27) that GST-DHFR is dimeric in solution and its dimerization interface is compatible with the crystallographic GST dimer possessing the twofold symmetry axis. The experimental scattering pattern of GST-DHFR is shown in Fig. 4 C (left panel). The program BUNCH was employed to build the dimeric fusion protein accounting for the P2 symmetry from the high-resolution models of GST (PDB entry 1gta; see Ref. 45) and DHFR (1ra9; see Ref. 46) connected by the linker represented by 10 DRs. The GST monomer was fixed at the position yielding the proper dimerization interface. The search procedure was refining the position of the DHFR subdomain and the conformation of the DR chain representing the linker in the symmetry independent part. The resulting model yields a good fit to the experimental data of the fusion protein with χ = 0.97 and is also consistent with the earlier model where the entire DHFR portion of the fusion protein was reconstructed ab initio using dummy residues approach (the compatibility of the two models is demonstrated in Fig. 4 C, right panel). A typical BUNCH run required <2 h on a 2.2-GHz PC.

Ranking of the rigid body models using simplified docking criteria

As seen from the above examples, rigid body modeling may yield multiple solutions providing (nearly) the same fits to the experimental data. The requirements of interconnectivity, non-overlapping and information on distances between specific residues do reduce the ambiguity of model building but in some cases additional criteria may be indispensable. For multisubunit complexes, such criteria may be obtained by the analysis of the intersubunit interfaces.

Various approaches to analyze protein-protein interfaces have been developed largely utilizing a combination of energetics and shape complementarity. Analysis of the interaction sites using surface patches (47) uses solvation potential, residue interface propensity, hydrophobicity, planarity, protrusion and accessible surface area as parameters to rank the interfaces. Empirical scoring functions for structure-based binding affinity prediction (48) accounts for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. Amino acid compositions and sizes of the recognition sites of protein-protein complexes were analyzed in Chakrabarti and Janin (49) and Lo Conte et al. (50). The soft-docking algorithm (51) performs a complex type-dependent filtering of candidate binding modes on the basis of geometric matching, hydrophobicity and electrostatic complementarity. The method (52) employs a low-resolution rigid body Monte Carlo search followed by simultaneous optimization of backbone displacement and side chain conformations for the docking. The pairwise shape complementarity scoring function maximizing the total number of atom pairs between the receptor and the ligand within the distance cutoff is developed (53) and can be optimized together with disolvation free energy and electrostatics (54). Approaches for filtering and selection of structural models based on combining docking with biochemical and biophysical information (i.e., NMR data) are developed (55,56).

Most of the above methods are very useful for building and refining energetically sound detailed models, and the calculation of these criteria is usually computationally intensive. Given the low resolution of the SAXS method, our aim was to devise simple and fast methods estimating the quality of the intersubunit contacts, which could also be used as restraints during the rigid body modeling. The quality assessment is done using Cα-only representation of the molecules and considers the two aspects: shape complementarity and amino acid composition at the interface. Another potential aspect, charge complementarity, heavily relies on all-atom representation of the surfaces for accurate electrostatic energy calculations and would be rather inaccurate in Cα-only representation.

The shape complementarity criterion is formulated in terms of maximization of the total number of contacts Ncnt between distinct subunits made by pairs of their Cαs. It is assumed that two residues belonging to different subunits in a complex compose a pair if the distance between their Cα atoms does not exceed the average threshold of rcnt = 0.7 nm. This criterion is not residue-specific and is applicable to rank subunit arrangements without steric clashes only, as overlapping subunits would obviously yield higher number of the pairs.

As the interactions between the residues are the major driving force for protein-protein interface formation, a simplified residue-specific criterion was also introduced. The high-resolution structures of more than 80 protein complexes of different types (protein-inhibitor, antibody-antigen, homodimers, etc.) downloaded from the PDB were analyzed to get the average contact frequency between pairs of residues using the same threshold rcnt for assignment of a contacts to two residues. The average histogram of frequency distribution hex of 210 (= 20 × (20 + 1)/2) possible residue pairs should thus give information about which residues are more and less likely to be involved in the interface formation. For polar residues, the average frequency of observing a pair of oppositely charged residues was twice that for the residues having the same charge. This indicates that such a histogram, which is effectively a simplified version of residue-level potentials or an interface propensity table (47,49,50,57), also bears information about the charge complementarity.

Given a model of the complex, a histogram of its interface hobs can be computed and the correlation coefficient (CC) between hobs and hex can be used to assess the quality of the interface. The value CC is computed using the standard formula of

equation M27
(10)

where

equation M28

The CC value ranges between 1 and −1 and, on average, the higher the value, the better the interface.

The criteria were tested on several examples, in particular on the family of the best solutions of ZmPDC quaternary structure restored by GLOBSYMM. As this program allows for having minor overlaps between the monomers only histogram correlations were computed. The crystal structure of ZmPDC and the best model from GLOBSYMM (in Fig. 5 A, left panel, first and second rows) yield CC = 0.25 and 0.23, respectively. These values are higher than those calculated from most of other models top-ranked by GLOBSYMM. There is however a model yielding an even higher CC = 0.28 but a poorer fit to the experimental data than that from the two above models. Interestingly, this latter model (Fig. 5 A, left panel, third row) displays a correct arrangement of monomers within the dimer and, moreover, the architecture of the entire tetramer, which is very similar to that of a homologous PDC from yeast (PDB entry 1pvd; see Ref. 58). Fig. 5 A (left panel, bottom row) displays a model with an incorrect oligomerization interface, which yields a poorer docking criterion (CC = 0.18), though its fit to the experimental data is nearly as good as that of the best model (Fig. 5 A, right panel, magenta curve).

FIGURE 5
Screening of the multiple models provided by rigid body refinement methods. (A) Models of ZmPDC generated by GLOBSYMM. (Left panel, first row) Crystal structure of ZmPDC; (second row) the best GLOBSYMM model; (third row) the model with the highest CC; ...

The above example demonstrates the usefulness of the simplified docking criteria and similar results were also obtained in other tests. One must however be aware of limitations of such criteria, illustrated by the example below. A simulated scattering curve from the complex of bovine α-chymotrypsin with eglin C (PDB entry 1acb; see Ref. 59) displayed in Fig. 5 B was computed in the same way as described for GTS-tRNA. This curve was used for the modeling based on the structures of α-chymotrypsin (chain E) and eglin (chain I) as two rigid bodies. Multiple reconstructions were performed by SASREF, and the variety of solutions without steric clashes yielding good fits to the simulated data (in Fig. 5 B, right panel) were screened to find those having maximum number of contacts between the subunits and yielding the highest correlation coefficients. The two best models according to the docking criteria (Fig. 5 B, left panel, middle and right column) have the parameters Ncnt = 31 and 40 and CC = 0.40 and 0.31, respectively, which are apparently better than those of the crystallographic complex (Ncnt = 34 and CC = 0.19). Though the overall shapes of the SASREF models are similar to the crystallographic one, the relative orientation of the subunits in both models is different from that of the crystallographic complex (both yield RMSD = 1.2 nm). Admittedly, the arrangement of the two proteins in the crystallographic complex, where the contact is established via an extended loop Pro42–Arg48 of eglin, cannot be considered a typical intersubunit interface. Nevertheless, this example demonstrates the possibility of getting false-positives when using the simplified docking criteria. It is worth noting that addition of a distance constraint requiring proximity of Trp215 of chymotrypsin with Thr44 of eglin allows SASREF to obtain a unique solution with the eglin position shifted by δr = 0.22 nm and rotated by ω = 13° and within the RMSD = 0.1 nm from the crystal structure.

In test calculations on other simulated and practical examples, the use of simplified docking criteria did provide additional information for the selection of the correct model. Still, to avoid biasing the minimization algorithms toward the false positives it was decided not to include the Ncnt and CC criteria into the goal function at this stage but rather to compute and list these parameters for the best selected models. The algorithms rapidly computing the simplified docking criteria are also available as standalone programs for the screening of multiple models.

CONCLUSIONS

A versatile set of tools presented here allows one to rapidly construct rigid body models of macromolecular complexes with minimum user intervention while maximizing the information content in the scattering data (multiple curves fitting) and adding information from other sources (symmetry, distance restraints, docking criteria). With recent instrumental and methodical advances, rigid body refinement against SAS data indeed became a powerful method to study complexes, allowing, in many cases, unique results to be obtained. Given the limited resolution of SAS data, one should, however, bear in mind that the models constructed by rigid body refinement, although built from high-resolution domains, are still low-resolution models. In particular, one should bear in mind that obtaining a configuration of subunits corresponding to an enanthiomorphous structure is always possible. Potential limitations of the technique and the possibility of obtaining multiple solutions compatible with the experimental data are presented here as a word of caution in using the rigid body refinement methods. In some cases, multiple runs of the programs and ranking of the models according to their biological relevance are indispensable for the cross-validation of the results.

Most of the methods presented here can be used for the analysis of x-ray and neutron scattering data from complexes of proteins, nucleic acids, and other biological macromolecules. The programs DIMFOM, GLOBSYM, and SASREF require precomputed x-ray or neutron scattering amplitudes from the domains/subunits, which can be done by the programs CRYSOL or CRYSON, respectively. The power of specific deuteration of contrast variation in neutron scattering can thus be fully exploited. The only exception is the program BUNCH, which explicitly utilizes the x-ray form factor of a dummy residue to compute the scattering from missing loops and can thus only be used for the x-ray data.

Regarding the relation between the brute-force and the heuristic methods, there are advantages and shortcomings in both of them. In default operation modes, the brute-force methods are faster, but in principle they may miss the global minimum because of the finite grid sampling. A combination of the two approaches is possible, where the heuristic method is started in the refinement mode (i.e., at a low temperature) from the solution obtained by a brute-force method. Of course, the applicability of the latter methods is limited to one subunit per asymmetric unit, whereas the heuristic algorithms can handle more complex systems.

The programs described in the present article can be downloaded as precompiled executables for all major computer platforms (from http://www.embl-hamburg.de/ExternalInfo/Research/Sax/software.html). These rigid body analysis programs have been made available to the biological community as beta-versions some time ago, and valuable feedback from the users has already been received. We have also used these beta-versions in several experimental projects (6062), where they did provide a significant advantage over the earlier rigid body modeling methods. The present versions of the programs include numerous modifications and improvements from the beta-testing phase.

Acknowledgments

The work was supported in part by the European Union Structural Proteomics in Europe contract No. QLG2-CT-2002-00988.

References

1. Gerstein, M., A. Edwards, C. H. Arrowsmith, and G. T. Montelione. 2003. Structural genomics: current progress. Science. 299:1663. [PubMed]
2. Sali, A., R. Glaeser, T. Earnest, and W. Baumeister. 2003. From words to literature in structural proteomics. Nature. 422:216–225. [PubMed]
3. Aloy, P., B. Bottcher, H. Ceulemans, C. Leutwein, C. Mellwig, S. Fischer, A. C. Gavin, P. Bork, G. Superti-Furga, L. Serrano, and R. B. Russell. 2004. Structure-based assembly of protein complexes in yeast. Science. 303:2026–2029. [PubMed]
4. Feigin, L. A., and D. I. Svergun. 1987. Structure analysis by small-angle x-ray and neutron scattering. Plenum Press, New York.
5. Svergun, D. I., and M. H. J. Koch. 2003. Small angle scattering studies of biological macromolecules in solution. Rep. Prog. Phys. 66:1735–1782.
6. Svergun, D. I., V. V. Volkov, M. B. Kozin, and H. B. Stuhrmann. 1996. New developments in direct shape determination from small-angle scattering. II. Uniqueness. Acta Crystallogr. A52:419–426.
7. Chacon, P., F. Moran, J. F. Diaz, E. Pantos, and J. M. Andreu. 1998. Low-resolution structures of proteins in solution retrieved from x-ray scattering with a genetic algorithm. Biophys. J. 74:2760–2775. [PMC free article] [PubMed]
8. Walther, D., F. E. Cohen, and S. Doniach. 2000. Reconstruction of low-resolution three-dimensional density maps from one-dimensional small-angle x-ray solution scattering data for biomolecules. J. Appl. Crystallogr. 33:350–363.
9. Heller, W. T., E. Abusamhadneh, N. Finley, P. R. Rosevear, and J. Trewhella. 2002. The solution structure of a cardiac troponin C-troponin I-troponin T complex shows a somewhat compact troponin C interacting with an extended troponin I-troponin T component. Biochemistry. 41:15654–15663. [PubMed]
10. Svergun, D. I. 1999. Restoring low-resolution structure of biological macromolecules from solution scattering using simulated annealing. Biophys. J. 76:2879–2886. [PMC free article] [PubMed]
11. Svergun, D. I., M. V. Petoukhov, and M. H. J. Koch. 2001. Determination of domain structure of proteins from x-ray solution scattering. Biophys. J. 80:2946–2953. [PMC free article] [PubMed]
12. Wall, M. E., S. C. Gallagher, and J. Trewhella. 2000. Large-scale shape changes in proteins and macromolecular complexes. Annu. Rev. Phys. Chem. 51:355–380. [PubMed]
13. Boehm, M. K., J. M. Woof, M. A. Kerr, and S. J. Perkins. 1999. The Fab and Fc fragments of IgA1 exhibit a different arrangement from that in IgG: a study by x-ray and neutron solution scattering and homology modeling. J. Mol. Biol. 286:1421–1447. [PubMed]
14. Sun, Z., K. B. Reid, and S. J. Perkins. 2004. The dimeric and trimeric solution structures of the multidomain complement protein properdin by x-ray scattering, analytical ultracentrifugation and constrained modeling. J. Mol. Biol. 343:1327–1343. [PubMed]
15. Svergun, D. I., C. Barberato, and M. H. J. Koch. 1995. CRYSOL—a program to evaluate x-ray solution scattering of biological macromolecules from atomic coordinates. J. Appl. Crystallogr. 28:768–773.
16. Svergun, D. I., S. Richard, M. H. J. Koch, Z. Sayers, S. Kuprin, and G. Zaccai. 1998. Protein hydration in solution: experimental observation by x-ray and neutron scattering. Proc. Natl. Acad. Sci. USA. 95:2267–2272. [PMC free article] [PubMed]
17. Svergun, D. I. 1994. Solution scattering from biopolymers: advanced contrast variation data analysis. Acta Crystallogr. A50:391–402.
18. Kozin, M. B., and D. I. Svergun. 2000. A software system for automated and interactive rigid body modeling of solution scattering data. J. Appl. Crystallogr. 33:775–777.
19. Konarev, P. V., M. V. Petoukhov, and D. I. Svergun. 2001. MASSHA—a graphic system for rigid body modeling of macromolecular complexes against solution scattering data. J. Appl. Crystallogr. 34:527–532.
20. Krueger, J. K., S. C. Gallagher, C. A. Wang, and J. Trewhella. 2000. Calmodulin remains extended upon binding to smooth muscle caldesmon: a combined small-angle scattering and Fourier transform infrared spectroscopy study. Biochemistry. 39:3979–3987. [PubMed]
21. Tung, C. S., D. A. Walsh, and J. Trewhella. 2002. A structural model of the catalytic subunit-regulatory subunit dimeric complex of the cAMP-dependent protein kinase. J. Biol. Chem. 277:12423–12431. [PubMed]
22. Mattinen, M. L., K. Paakkonen, T. Ikonen, J. Craven, T. Drakenberg, R. Serimaa, J. Waltho, and A. Annila. 2002. Quaternary structure built from subunits combining NMR and small-angle x-ray scattering data. Biophys. J. 83:1177–1183. [PMC free article] [PubMed]
23. Svergun, D. I. 1997. Restoring three-dimensional structure of biopolymers from solution scattering. J. Appl. Crystallogr. 30:792–797.
24. Svergun, D. I. 1991. Mathematical methods in small-angle scattering data analysis. J. Appl. Crystallogr. 24:485–492.
25. Svergun, D. I., V. V. Volkov, M. B. Kozin, H. B. Stuhrmann, C. Barberato, and M. H. J. Koch. 1997. Shape determination from solution scattering of biopolymers. J. Appl. Crystallogr. 30:798–802.
26. Kirkpatrick, S., C. D. Gelatt, Jr., and M. P. Vecci. 1983. Optimization by simulated annealing. Science. 220:671–680. [PubMed]
27. Petoukhov, M. V., N. A. Eady, K. A. Brown, and D. I. Svergun. 2002. Addition of missing loops and domains to protein models by x-ray solution scattering. Biophys. J. 83:3113–3125. [PMC free article] [PubMed]
28. Press, W. H., S. A. Teukolsky, W. T. Wetterling, and B. P. Flannery. 1992. Numerical Recipes. University Press, Cambridge.
29. Ingber, L. 1993. Simulated annealing: practice versus theory. Math. Comput. Model. 18:29–57.
30. Boulin, C., R. Kempf, M. H. J. Koch, and S. M. McLaughlin. 1986. Data appraisal, evaluation and display for synchrotron radiation experiments: hardware and software. Nucl. Instrum. Methods A. 249:399–407.
31. Boulin, C. J., R. Kempf, A. Gabriel, and M. H. J. Koch. 1988. Data acquisition systems for linear and area x-ray detectors using delay line readout. Nucl. Instrum. Methods A. 269:312–320.
32. Koch, M. H. J., and J. Bordas. 1983. X-ray diffraction and scattering on disordered systems using synchrotron radiation. Nucl. Instrum. Methods. 208:461–469.
33. Depautex, C., P. Desvignes, P. Leboucher, M. Lemonnier, D. Dagenaux, J. P. Benoit, and P. Vachette. 1987. The small angle x-ray scattering instrument D24. CNRS Annual Report, LURE, Orsay, France.
34. Svergun, D. I., I. Aldag, T. Sieck, K. Altendorf, M. H. J. Koch, D. J. Kane, M. B. Kozin, and G. Grueber. 1998. A model of the quaternary structure of the Escherichia coli F1 ATPase from x-ray solution scattering and evidence for structural changes in the δ-subunit during ATP hydrolysis. Biophys. J. 75:2212–2219. [PMC free article] [PubMed]
35. Svergun, D. I., M. V. Petoukhov, M. H. J. Koch, and S. Koenig. 2000. Crystal versus solution structures of thiamine diphosphate-dependent enzymes. J. Biol. Chem. 275:297–302. [PubMed]
36. Dainese, E., D. Svergun, M. Beltramini, P. Di Muro, and B. Salvato. 2000. Low-resolution structure of the proteolytic fragments of the Rapana venosa hemocyanin in solution. Arch. Biochem. Biophys. 373:154–162. [PubMed]
37. Marquez, J. A., C. I. Smith, M. V. Petoukhov, P. Lo Surdo, P. T. Mattsson, M. Knekt, A. Westlund, K. Scheffzek, M. Saraste, and D. I. Svergun. 2003. Conformation of full-length Bruton tyrosine kinase (Btk) from synchrotron x-ray solution scattering. EMBO J. 22:4616–4624. [PMC free article] [PubMed]
38. Berman, H. M., J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne. 2000. The Protein Data Bank. Nucleic Acids Res. 28:235–242. [PMC free article] [PubMed]
39. Sekine, S., O. Nureki, A. Shimada, D. G. Vassylyev, and S. Yokoyama. 2001. Structural basis for anticodon recognition by discriminating glutamyl-tRNA synthetase. Nat. Struct. Biol. 8:203–206. [PubMed]
40. Engelbrecht, S., and W. Junge. 1997. ATP synthase: a tentative structural model. FEBS Lett. 414:485–491. [PubMed]
41. Deckers-Hebestreit, G., and K. Altendorf. 1996. The F0F1-type ATP synthases of bacteria: structure and function of the F0 complex. Annu. Rev. Microbiol. 50:791–824. [PubMed]
42. Gibbons, C., M. G. Montgomery, A. G. Leslie, and J. E. Walker. 2000. The structure of the central stalk in bovine F1-ATPase at 2.4 Å resolution. Nat. Struct. Biol. 7:1055–1061. [PubMed]
43. Dobritzsch, D., S. Konig, G. Schneider, and G. Lu. 1998. High resolution crystal structure of pyruvate decarboxylase from Zymomonas mobilis. Implications for substrate activation in pyruvate decarboxylases. J. Biol. Chem. 273:20196–20204. [PubMed]
44. Cuff, M. E., K. I. Miller, K. E. van Holde, and W. A. Hendrickson. 1998. Crystal structure of a functional unit from Octopus hemocyanin. J. Mol. Biol. 278:855–870. [PubMed]
45. McTigue, M. A., D. R. Williams, and J. A. Tainer. 1995. Crystal structures of a schistosomal drug and vaccine target: glutathione S-transferase from Schistosoma japonica and its complex with the leading antischistosomal drug praziquantel. J. Mol. Biol. 246:21–27. [PubMed]
46. Sawaya, M. R., and J. Kraut. 1997. Loop and subdomain movements in the mechanism of Escherichia coli dihydrofolate reductase: crystallographic evidence. Biochemistry. 36:586–603. [PubMed]
47. Jones, S., and J. M. Thornton. 1997. Analysis of protein-protein interaction sites using surface patches. J. Mol. Biol. 272:121–132. [PubMed]
48. Wang, R., L. Lai, and S. Wang. 2002. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J. Comput. Aided Mol. Des. 16:11–26. [PubMed]
49. Chakrabarti, P., and J. Janin. 2002. Dissecting protein-protein recognition sites. Proteins. 47:334–343. [PubMed]
50. Lo Conte, L., C. Chothia, and J. Janin. 1999. The atomic structure of protein-protein recognition sites. J. Mol. Biol. 285:2177–2198. [PubMed]
51. Li, C. H., X. H. Ma, W. Z. Chen, and C. X. Wang. 2003. A protein-protein docking algorithm dependent on the type of complexes. Protein Eng. 16:265–269. [PubMed]
52. Gray, J. J., S. Moughon, C. Wang, O. Schueler-Furman, B. Kuhlman, C. A. Rohl, and D. Baker. 2003. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J. Mol. Biol. 331:281–299. [PubMed]
53. Chen, R., and Z. Weng. 2003. A novel shape complementarity scoring function for protein-protein docking. Proteins. 51:397–408. [PubMed]
54. Chen, R., and Z. Weng. 2002. Docking unbound proteins using shape complementarity, desolvation, and electrostatics. Proteins. 47:281–294. [PubMed]
55. Dominguez, C., R. Boelens, and A. M. Bonvin. 2003. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc. 125:1731–1737. [PubMed]
56. Dobrodumov, A., and A. M. Gronenborn. 2003. Filtering and selection of structural models: combining docking and NMR. Proteins. 53:18–32. [PubMed]
57. Moont, G., H. A. Gabb, and M. J. Sternberg. 1999. Use of pair potentials across protein interfaces in screening predicted docked complexes. Proteins. 35:364–373. [PubMed]
58. Arjunan, P., T. Umland, F. Dyda, S. Swaminathan, W. Furey, M. Sax, B. Farrenkopf, Y. Gao, D. Zhang, and F. Jordan. 1996. Crystal structure of the thiamin diphosphate-dependent enzyme pyruvate decarboxylase from the yeast Saccharomyces cerevisiae at 2.3 Å resolution. J. Mol. Biol. 256:590–600. [PubMed]
59. Frigerio, F., A. Coda, L. Pugliese, C. Lionetti, E. Menegatti, G. Amiconi, H. P. Schnebli, P. Ascenzi, and M. Bolognesi. 1992. Crystal and molecular structure of the bovine α-chymotrypsin-eglin C complex at 2.0 Å resolution. J. Mol. Biol. 225:107–123. [PubMed]
60. Petoukhov, M. V., D. I. Svergun, P. V. Konarev, S. Ravasio, R. H. van den Heuvel, B. Curti, and M. A. Vanoni. 2003. Quaternary structure of Azospirillum brasilense NADPH-dependent glutamate synthase in solution as revealed by synchrotron radiation x-ray scattering. J. Biol. Chem. 278:29933–29939. [PubMed]
61. Rosano, C., S. Zuccotti, B. Cobucci-Ponzano, M. Mazzone, M. Rossi, M. Moracci, M. V. Petoukhov, D. I. Svergun, and M. Bolognesi. 2004. Structural characterization of the nonameric assembly of an archaeal α-L-fucosidase by synchrotron small angle x-ray scattering. Biochem. Biophys. Res. Commun. 320:176–182. [PubMed]
62. Bilecen, K., U. H. Ozturk, A. D. Duru, T. Sutlu, M. V. Petoukhov, D. I. Svergun, M. H. Koch, U. O. Sezerman, I. Cakmak, and Z. Sayers. 2005. Triticum durum metallothionein: isolation of the gene and structural characterization of the protein using solution scattering and molecular modeling. J. Biol. Chem. 280:13701–13711. [PubMed]

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