Scoring protein interaction decoys using exposed residues (SPIDER): a novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues

Accurate prediction of the structure of protein-protein complexes in computational docking experiments remains a formidable challenge. It has been recognized that identifying native or native-like poses among multiple decoys is the major bottleneck of the current scoring functions used in docking. We have developed a novel multibody pose-scoring function that has no theoretical limit on the number of residues contributing to the individual interaction terms. We use a coarse-grain representation of a protein-protein complex where each residue is represented by its side chain centroid. We apply a computational geometry approach called Almost-Delaunay tessellation that transforms protein-protein complexes into a residue contact network, or an undirectional graph where vertex-residues are nodes connected by edges. This treatment forms a family of interfacial graphs representing a dataset of protein-protein complexes. We then employ frequent subgraph mining approach to identify common interfacial residue patterns that appear in at least a subset of native protein-protein interfaces. The geometrical parameters and frequency of occurrence of each "native" pattern in the training set are used to develop the new SPIDER scoring function. SPIDER was validated using standard "ZDOCK" benchmark dataset that was not used in the development of SPIDER. We demonstrate that SPIDER scoring function ranks native and native-like poses above geometrical decoys and that it exceeds in performance a popular ZRANK scoring function. SPIDER was ranked among the top scoring functions in a recent round of CAPRI (Critical Assessment of PRedicted Interactions) blind test of protein-protein docking methods.     

Optimal docking area: a new method for predicting protein-protein interaction sites

Understanding energetics and mechanism of protein-protein association remains one of the biggest theoretical problems in structural biology. It is assumed that desolvation must play an essential role during the association process, and indeed protein-protein interfaces in obligate complexes have been found to be highly hydrophobic. However, the identification of protein interaction sites from surface analysis of proteins involved in non-obligate protein-protein complexes is more challenging. Here we present Optimal Docking Area (ODA), a new fast and accurate method of analyzing a protein surface in search of areas with favorable energy change when buried upon protein-protein association. The method identifies continuous surface patches with optimal docking desolvation energy based on atomic solvation parameters adjusted for protein-protein docking. The procedure has been validated on the unbound structures of a total of 66 non-homologous proteins involved in non-obligate protein-protein hetero-complexes of known structure. Optimal docking areas with significant low-docking surface energy were found in around half of the proteins. The 'ODA hot spots' detected in X-ray unbound structures were correctly located in the known protein-protein binding sites in 80% of the cases. The role of these low-surface-energy areas during complex formation is discussed. Burial of these regions during protein-protein association may favor the complexed configurations with near-native interfaces but otherwise arbitrary orientations, thus driving the formation of an encounter complex. The patch prediction procedure is freely accessible at http://www.molsoft.com/oda and can be easily scaled up for predictions in structural proteomics.     

Hydrophobic complementarity in protein-protein docking

Formation of hydrophobic contacts across a newly formed interface is energetically favorable. Based on this observation we developed a geometric-hydrophobic docking algorithm that estimates quantitatively the hydrophobic complementarity at protein-protein interfaces. Each molecule to be docked is represented as a grid of complex numbers, storing information regarding the shape of the molecule in the real part and information regarding the hydropathy of the surface in the imaginary part. The grid representations are correlated using fast Fourier transformations. The algorithm is used to compare the extent of hydrophobic complementarity in oligomers (represented by D2 tetramers) and in hetero-dimers of soluble proteins (complexes). We also test the implication of hydrophobic complementarity in distinguishing correct from false docking solutions. We find that hydrophobic complementarity at the interface exists in oligomers and in complexes, and in both groups the extent of such complementarity depends on the size of the interface. Thus, the non-polar portions of large interfaces are more often juxtaposed than non-polar portions of small interfaces. Next we find that hydrophobic complementarity helps to point out correct docking solutions. In oligomers it significantly improves the ranks of nearly correct reassembled and modeled tetramers. Combining geometric, electrostatic and hydrophobic complementarity for complexes gives excellent results, ranking a nearly correct solution < 10 for 5 of 23 tested systems, < 100 for 8 systems and < 1000 for 19 systems.     

pyDock: electrostatics and desolvation for effective scoring of rigid-body protein-protein docking

The accurate scoring of rigid-body docking orientations represents one of the major difficulties in protein-protein docking prediction. Other challenges are the development of faster and more efficient sampling methods and the introduction of receptor and ligand flexibility during simulations. Overall, good discrimination of near-native docking poses from the very early stages of rigid-body protein docking is essential step before applying more costly interface refinement to the correct docking solutions. Here we explore a simple approach to scoring of rigid-body docking poses, which has been implemented in a program called pyDock. The scheme is based on Coulombic electrostatics with distance dependent dielectric constant, and implicit desolvation energy with atomic solvation parameters previously adjusted for rigid-body protein-protein docking. This scoring function is not highly dependent on specific geometry of the docking poses and therefore can be used in rigid-body docking sets generated by a variety of methods. We have tested the procedure in a large benchmark set of 80 unbound docking cases. The method is able to detect a near-native solution from 12,000 docking poses and place it within the 100 lowest-energy docking solutions in 56% of the cases, in a completely unrestricted manner and without any other additional information. More specifically, a near-native solution will lie within the top 20 solutions in 37% of the cases. The simplicity of the approach allows for a better understanding of the physical principles behind protein-protein association, and provides a fast tool for the evaluation of large sets of rigid-body docking poses in search of the near-native orientation.     

Protein-protein recognition analyzed by docking simulation.

Antibody-lysozyme and protease-inhibitor complexes are reconstituted by docking lysozyme as a rigid body onto the combining site of the antibodies and the inhibitors onto the active site of the proteases. Simplified protein models with one sphere per residue are subjected to simulated annealing using a crude energy function where the attractive component is proportional to the interface area. The procedure finds clusters of orientations in which a steric fit between the two protein components is achieved over a large contact surface. With five out of six complexes, the native structure of the complexes determined by X-ray crystallography is among those retained. Docked complexes are then subjected to conformational energy refinement with full atomic detail. With Fab HyHEL 5 and lysozyme, a native-like complex has the lowest refined energy. It can also be retrieved when starting with the X-ray structure of free lysozyme. However, some non-native complexes cannot be rejected: they form large interfaces, have a large number of H-bonds, and few unpaired polar groups. While these are necessary features of protein-protein recognition, they are not sufficient in determining specificity.     

Evaluation of protein docking predictions using Hex 3.1 in CAPRI rounds 1 and 2

This article describes and reviews our efforts using Hex 3.1 to predict the docking modes of the seven target protein-protein complexes presented in the CAPRI (Critical Assessment of Predicted Interactions) blind docking trial. For each target, the structure of at least one of the docking partners was given in its unbound form, and several of the targets involved large multimeric structures (e.g., Lactobacillus HPr kinase, hemagglutinin, bovine rotavirus VP6). Here we describe several enhancements to our original spherical polar Fourier docking correlation algorithm. For example, a novel surface sphere smothering algorithm is introduced to generate multiple local coordinate systems around the surface of a large receptor molecule, which may be used to define a small number of initial ligand-docking orientations distributed over the receptor surface. High-resolution spherical polar docking correlations are performed over the resulting receptor surface patches, and candidate docking solutions are refined by using a novel soft molecular mechanics energy minimization procedure. Overall, this approach identified two good solutions at rank 5 or less for two of the seven CAPRI complexes. Subsequent analysis of our results shows that Hex 3.1 is able to place good solutions within a list of 相似文献   

Insights from the structural analysis of protein heterodimer interfaces

Protein heterodimer complexes are often involved in catalysis, regulation, assembly, immunity and inhibition. This involves the formation of stable interfaces between the interacting partners. Hence, it is of interest to describe heterodimer interfaces using known structural complexes. We use a non-redundant dataset of 192 heterodimer complex structures from the protein databank (PDB) to identify interface residues and describe their interfaces using amino-acids residue property preference. Analysis of the dataset shows that the heterodimer interfaces are often abundant in polar residues. The analysis also shows the presence of two classes of interfaces in heterodimer complexes. The first class of interfaces (class A) with more polar residues than core but less than surface is known. These interfaces are more hydrophobic than surfaces, where protein-protein binding is largely hydrophobic. The second class of interfaces (class B) with more polar residues than core and surface is shown. These interfaces are more polar than surfaces, where binding is mainly polar. Thus, these findings provide insights to the understanding of protein-protein interactions.     

Protein docking using a genetic algorithm

A genetic algorithm (GA) for protein-protein docking is described, in which the proteins are represented by dot surfaces calculated using the Connolly program. The GA is used to move the surface of one protein relative to the other to locate the area of greatest surface complementarity between the two. Surface dots are deemed complementary if their normals are opposed, their Connolly shape type is complementary, and their hydrogen bonding or hydrophobic potential is fulfilled. Overlap of the protein interiors is penalized. The GA is tested on 34 large protein-protein complexes where one or both proteins has been crystallized separately. Parameters are established for which 30 of the complexes have at least one near-native solution ranked in the top 100. We have also successfully reassembled a 1,400-residue heptamer based on the top-ranking GA solution obtained when docking two bound subunits.     

Statistical analysis of predominantly transient protein-protein interfaces

A non-redundant set of 170 protein-protein interfaces of known structure was statistically analyzed for residue and secondary-structure compositions, pairing preferences and side-chain-backbone interaction frequencies. By focussing mainly on transient protein-protein interfaces, the results underline previous findings for protein-protein interfaces but also show some new interesting aspects of transient interfaces. The residue compositions at interfaces found in this study correlate well with the results of other studies. On average, contacts between pairs of hydrophobic and polar residues were unfavorable, and the charged residues tended to pair subject to charge complementarity. Secondary structure composition analysis shows that neither helices nor beta-sheets are dominantly populated at interfaces. Analyzing the pairing preferences of the secondary structure elements revealed a higher affinity within the same elements and alludes to tight packings. In addition, the results for the side-chain and backbone interaction frequencies, which were measured under more stringent conditions, showed a high occurrence of side-chain-backbone interactions. Taking a closer look at the helix and beta-sheet binding frequencies for a given side-chain and backbone interaction underlined the relevance of tight packings. The polarity of interfaces increased with decreasing interface size. These types of information may be useful for scoring complexes in protein-protein docking studies or for prediction of protein-protein interfaces from the sequences alone.     

A knowledge-based forcefield for protein-protein interface design

A distance-dependent knowledge-based potential for protein-protein interactions is derived and tested for application in protein design. Information on residue type specific C(alpha) and C(beta) pair distances is extracted from complex crystal structures in the Protein Data Bank and used in the form of radial distribution functions. The use of only backbone and C(beta) position information allows generation of relative protein-protein orientation poses with minimal sidechain information. Further coarse-graining can be done simply in the same theoretical framework to give potentials for residues of known type interacting with unknown type, as in a one-sided interface design problem. Both interface design via pose generation followed by sidechain repacking and localized protein-protein docking tests are performed on 39 nonredundant antibody-antigen complexes for which crystal structures are available. As reference, Lennard-Jones potentials, unspecific for residue type and biasing toward varying degrees of residue pair separation are used as controls. For interface design, the knowledge-based potentials give the best combination of consistently designable poses, low RMSD to the known structure, and more tightly bound interfaces with no added computational cost. 77% of the poses could be designed to give complexes with negative free energies of binding. Generally, larger interface separation promotes designability, but weakens the binding of the resulting designs. A localized docking test shows that the knowledge-based nature of the potentials improves performance and compares respectably with more sophisticated all-atoms potentials.     

The relationship between the flexibility of proteins and their conformational states on forming protein-protein complexes with an application to protein-protein docking

We investigate the extent to which the conformational fluctuations of proteins in solution reflect the conformational changes that they undergo when they form binary protein-protein complexes. To do this, we study a set of 41 proteins that form such complexes and whose three-dimensional structures are known, both bound in the complex and unbound. We carry out molecular dynamics simulations of each protein, starting from the unbound structure, and analyze the resulting conformational fluctuations in trajectories of 5 ns in length, comparing with the structure in the complex. It is found that fluctuations take some parts of the molecules into regions of conformational space close to the bound state (or give information about it), but at no point in the simulation does each protein as whole sample the complete bound state. Subsequent use of conformations from a clustered MD ensemble in rigid-body docking is nevertheless partially successful when compared to docking the unbound conformations, as long as the unbound conformations are themselves included with the MD conformations and the whole globally rescored. For one key example where sub-domain motion is present, a ribonuclease inhibitor, principal components analysis of the MD was applied and was also able to produce conformations for docking that gave enhanced results compared to the unbound. The most significant finding is that core interface residues show a tendency to be less mobile (by size of fluctuation or entropy) than the rest of the surface even when the other binding partner is absent, and conversely the peripheral interface residues are more mobile. This surprising result, consistent across up to 40 of the 41 proteins, suggests different roles for these regions in protein recognition and binding, and suggests ways that docking algorithms could be improved by treating these regions differently in the docking process.     

Efficient comprehensive scoring of docked protein complexes using probabilistic support vector machines

Biological systems and processes rely on a complex network of molecular interactions. While the association of biological macromolecules is a fundamental biochemical phenomenon crucial for the understanding of complex living systems, protein-protein docking methods aim for the computational prediction of protein complexes from individual subunits. Docking algorithms generally produce large numbers of putative protein complexes with only few of these conformations resembling the native complex structure within an acceptable degree of structural similarity. A major challenge in the field of docking is to extract near-native structure(s) out of the large pool of solutions, the so called scoring or ranking problem. A series of structural, chemical, biological and physical properties are used in this work to classify docked protein-protein complexes. These properties include specialized energy functions, evolutionary relationship, class specific residue interface propensities, gap volume, buried surface area, empiric pair potentials on residue and atom level as well as measures for the tightness of fit. Efficient comprehensive scoring functions have been developed using probabilistic Support Vector Machines in combination with this array of properties on the largest currently available protein-protein docking benchmark. The established classifiers are shown to be specific for certain types of protein-protein complexes and are able to detect near-native complex conformations from large sets of decoys with high sensitivity. Using classification probabilities the ranking of near-native structures was drastically improved, leading to a significant enrichment of near-native complex conformations within the top ranks. It could be shown that the developed schemes outperform five other previously published scoring functions.     

Expanding GPCR homology model binding sites via a balloon potential: A molecular dynamics refinement approach

Homology modeling of G protein-coupled receptors is becoming a widely used tool in drug discovery. However, unrefined models built using the bovine rhodopsin crystal structure as the template, often have binding sites that are too small to accommodate known ligands. Here, we present a novel systematic method to refine model active sites based on a pressure-guided molecular dynamics simulation. A distinct advantage of this approach is the ability to introduce systematic perturbations in model backbone atoms in addition to side chain adjustments. The method is validated on two test cases: (1) docking of retinal into an MD-relaxed structure of opsin and (2) docking of known ligands into a homology model of the CCR2 receptor. In both cases, we show that the MD expansion algorithm makes it possible to dock the ligands in poses that agree with the crystal structure or mutagenesis data.     

Identification of protein-protein interaction sites from docking energy landscapes

Protein recognition is one of the most challenging and intriguing problems in structural biology. Despite all the available structural, sequence and biophysical information about protein-protein complexes, the physico-chemical patterns, if any, that make a protein surface likely to be involved in protein-protein interactions, remain elusive. Here, we apply protein docking simulations and analysis of the interaction energy landscapes to identify protein-protein interaction sites. The new protocol for global docking based on multi-start global energy optimization of an all-atom model of the ligand, with detailed receptor potentials and atomic solvation parameters optimized in a training set of 24 complexes, explores the conformational space around the whole receptor without restrictions. The ensembles of the rigid-body docking solutions generated by the simulations were subsequently used to project the docking energy landscapes onto the protein surfaces. We found that highly populated low-energy regions consistently corresponded to actual binding sites. The procedure was validated on a test set of 21 known protein-protein complexes not used in the training set. As much as 81% of the predicted high-propensity patch residues were located correctly in the native interfaces. This approach can guide the design of mutations on the surfaces of proteins, provide geometrical details of a possible interaction, and help to annotate protein surfaces in structural proteomics.     

Side-chain conformational changes upon Protein-Protein Association

Conformational changes upon protein-protein association are the key element of the binding mechanism. The study presents a systematic large-scale analysis of such conformational changes in the side chains. The results indicate that short and long side chains have different propensities for the conformational changes. Long side chains with three or more dihedral angles are often subject to large conformational transition. Shorter residues with one or two dihedral angles typically undergo local conformational changes not leading to a conformational transition. A relationship between the local readjustments and the equilibrium fluctuations of a side chain around its unbound conformation is suggested. Most of the side chains undergo larger changes in the dihedral angle most distant from the backbone. The frequencies of the core-to-surface interface transitions of six nonpolar residues and Tyr are larger than the frequencies of the opposite surface-to-core transitions. The binding increases both polar and nonpolar interface areas. However, the increase of the nonpolar area is larger for all considered classes of protein complexes, suggesting that the protein association perturbs the unbound interfaces to increase the hydrophobic contribution to the binding free energy. To test modeling approaches to side-chain flexibility in protein docking, conformational changes in the X-ray set were compared with those in the docking decoy sets. The results lead to a better understanding of the conformational changes in proteins and suggest directions for efficient conformational sampling in docking protocols.     

Identification of protein interaction partners and protein-protein interaction sites

Rigid-body docking has become quite successful in predicting the correct conformations of binary protein complexes, at least when the constituent proteins do not undergo large conformational changes upon binding. However, determining whether two given proteins interact is a more difficult problem. Successful docking procedures often give equally good scores for proteins that do not interact experimentally. This is the case for the multiple minimization approach we use here. An analysis of the results where all proteins within a set are docked with all other proteins (complete cross-docking) shows that the predictions can be greatly improved if the location of the correct binding interface on each protein is known, since the experimental complexes are much more likely to bring these two interfaces into contact, at the same time as yielding good interaction energy scores. While various methods exist for identifying binding interfaces, it is shown that simply studying the interaction of all potential protein pairs within a data set can itself help to identify the correct interfaces.     

Studies of protein-protein interfaces: a statistical analysis of the hydrophobic effect.

Data sets of 362 structurally nonredundant protein-protein interfaces and of 57 symmetry-related oligomeric interfaces have been used to explore whether the hydrophobic effect that guides protein folding is also the main driving force for protein-protein associations. The buried nonpolar surface area has been used to measure the hydrophobic effect. Our analysis indicates that, although the hydrophobic effect plays a dominant role in protein-protein binding, it is not as strong as that observed in the interior of protein monomers. Comparison of interiors of the monomers with those of the interfaces reveals that, in general, the hydrophobic amino acids are more frequent in the interior of the monomers than in the interior of the protein-protein interfaces. On the other hand, a higher proportion of charged and polar residues are buried at the interfaces, suggesting that hydrogen bonds and ion pairs contribute more to the stability of protein binding than to that of protein folding. Moreover, comparison of the interior of the interfaces to protein surfaces indicates that the interfaces are poorer in polar/charged than the surfaces and are richer in hydrophobic residues. The interior of the interfaces appears to constitute a compromise between the stabilization contributed by the hydrophobic effect on the one hand and avoiding patches on the protein surfaces that are too hydrophobic on the other. Such patches would be unfavorable for the unassociated monomers in solution. We conclude that, although the types of interactions are similar between protein-protein interfaces and single-chain proteins overall, the contribution of the hydrophobic effect to protein-protein associations is not as strong as to protein folding. This implies that packing patterns and interatom, or interresidue, pairwise potential functions, derived from monomers, are not ideally suited to predicting and assessing ligand associations or design. These would perform adequately only in cases where the hydrophobic effect at the binding site is substantial.     

Hydrophobic folding units at protein-protein interfaces: implications to protein folding and to protein-protein association.

A hydrophobic folding unit cutting algorithm, originally developed for dissecting single-chain proteins, has been applied to a dataset of dissimilar two-chain protein-protein interfaces. Rather than consider each individual chain separately, the two-chain complex has been treated as a single chain. The two-chain parsing results presented in this work show hydrophobicity to be a critical attribute of two-state versus three-state protein-protein complexes. The hydrophobic folding units at the interfaces of two-state complexes suggest that the cooperative nature of the two-chain protein folding is the outcome of the hydrophobic effect, similar to its being the driving force in a single-chain folding. In analogy to the protein-folding process, the two-chain, two-state model complex may correspond to the formation of compact, hydrophobic nuclei. On the other hand, the three-state model complex involves binding of already folded monomers, similar to the association of the hydrophobic folding units within a single chain. The similarity between folding entities in protein cores and in two-state protein-protein interfaces, despite the absence of some chain connectivities in the latter, indicates that chain linkage does not necessarily affect the native conformation. This further substantiates the notion that tertiary, non-local interactions play a critical role in protein folding. These compact, hydrophobic, two-chain folding units, derived from structurally dissimilar protein-protein interfaces, provide a rich set of data useful in investigations of the role played by chain connectivity and by tertiary interactions in studies of binding and of folding. Since they are composed of non-contiguous pieces of protein backbones, they may also aid in defining folding nuclei.     

A docking analysis of the statistical physics of protein-protein recognition

We describe protein-protein recognition within the frame of the random energy model of statistical physics. We simulate, by docking the component proteins, the process of association of two proteins that form a complex. We obtain the energy spectrum of a set of protein-protein complexes of known three-dimensional structure by performing docking in random orientations and scoring the models thus generated. We use a coarse protein representation where each amino acid residue is replaced by its Vorono? cell, and derive a scoring function by applying the evolutionary learning program ROGER to a set of parameters measured on that representation. Taking the scores of the docking models to be interaction energies, we obtain energy spectra for the complexes and fit them to a Gaussian distribution, from which we derive physical parameters such as a glass transition temperature and a specificity transition temperature.