ATTRACT: protein-protein docking in CAPRI using a reduced protein model

Protein-protein complex structures have been predicted for CAPRI Rounds 3 and 5 using a reduced protein model. Proteins are represented by up to 3 pseudoatoms per amino acid. The docking approach termed ATTRACT is based on energy minimization in translational and rotational degrees of freedom of one protein with respect to another protein. The reduced protein model allows one to perform systematic docking minimization of many thousand start structures in reasonable computer time. Flexibility of critical surface side-chains can be accounted for by a multiple conformational copy approach. The multicopy approach allows simultaneous adjustment of side-chain conformations and optimization of translational and rotational degrees of freedom of one protein with respect to the partner during docking. For 3 (Targets 8, 14, and 19) out of 5 CAPRI targets, the approach resulted in predictions in close agreement with experiment [root-mean-square deviation (RMSD) of backbone atoms within 10 A of the protein-protein interface < 1.8 A]. The comparison of predicted and experimental structures of the CAPRI targets indicates that besides local conformational changes (e.g., changes in side-chain conformations), global conformational changes of the protein backbone can be critical for complex formation. These conformational changes not accounted for during docking are a likely reason for the unrealistic predictions in 2 cases (Targets 9 and 18).     

Assessing the energy landscape of CAPRI targets by FunHunt

RosettaDock has repeatedly created high-resolution structures of protein complexes in the CAPRI experiment, thanks to the explicit modeling of conformational changes of the monomers at the side chain level. These models can be selected based on their energy. During the search for the lowest-energy model, RosettaDock samples a deep funnel around the native orientation, but additional funnels may appear in the energy landscape, especially in cases where backbone conformational changes occur upon binding. We have previously developed FunHunt, a Support Vector Machine-based classifier that distinguishes the energy funnels around the native orientation from other funnels in the energy landscape. Here we assess the ability of FunHunt to help in model selection in the CAPRI experiment. For all of 12 recent CAPRI targets, FunHunt clearly identifies a near-native funnel in comparison to the funnel around the lowest energy model identified by the RosettaDock global search protocol. FunHunt is also able to choose a near-native orientation among models submitted by predictor groups, demonstrating its general applicability for model selection. This suggests that FunHunt will be a valuable tool in coming CAPRI rounds for the selection of models, and for the definition of regions that need further refinement with restricted backbone flexibility.     

Computer simulations of cyclic enkephalin analogues

Molecular dynamics simulations and energy minimization studies of cyclic enkephalin analogues incorporating retro-inverso modifications have been carried out. The dynamic trajectories are analyzed in terms of the relative mobility of the 14-membered rings, conformational transitions among equilibrium states, and hydrogen-bonding patterns. The cyclization of the molecules reduces the motion of the ring structures substantially. Time-correlated conformational transitions resulting in the reorientation of peptide units are observed. Hydrogen bonds form principally C7 structures. Because of the incorporation of retro-inverso residues, C6 and C8 structures are also formed. Starting conformations for energy minimizations were obtained from the molecular dynamics simulations and from a systematic search of the conformational space available to the molecules. Several minimum energy backbone and side-chain conformations were found for each analogue. The effect of retro-inverso residues on hydrogen-bonding patterns and backbone conformations is discussed.     

Implicit flexibility in protein docking: cross-docking and local refinement

In previous CAPRI rounds (3-5) we showed that using MD-generated ensembles, as inputs for a rigid-body docking algorithm, increased our success rate, especially for targets exhibiting substantial amounts of induced fit. In recent rounds (6-11), our cross-docking was followed by a short MD-based local refinement for the subset of solutions with the lowest interaction energies after minimization. The above approach showed promising results for target 20, where we were able to recover 30% of native contacts for one of our submitted models. Further tests, performed a posteriori, revealed that cross-docking approach produces more near-native (NN) solutions but only for targets with large conformational changes upon binding. However, at the time of the blind docking experiment, these improved solutions were not chosen for the subsequent refinement, as their interaction energies after minimization ranked poorly compared with other solutions. This indicates deficiencies in the present scoring schemes that are based on interaction energies of minimized structures. Refinement MD simulations substantially increase the fraction of native contacts for NN docked solutions, but generally worsen interface and ligand RMSD. Further analysis shows that although MD simulations are able to improve sidechain packing across the interface, which results in an increased fraction of native contacts, they are not capable of improving interface and ligand backbone RMSD for NN structures beyond 1.5 and 3.5 A, respectively, even if explicit solvent is used.     

RosettaDock in CAPRI rounds 6-12

A challenge in protein-protein docking is to account for the conformational changes in the monomers that occur upon binding. The RosettaDock method, which incorporates sidechain flexibility but keeps the backbone fixed, was found in previous CAPRI rounds (4 and 5) to generate docking models with atomic accuracy, provided that conformational changes were mainly restricted to protein sidechains. In the recent rounds of CAPRI (6-12), large backbone conformational changes occur upon binding for several target complexes. To address these challenges, we explicitly introduced backbone flexibility in our modeling procedures by combining rigid-body docking with protein structure prediction techniques such as modeling variable loops and building homology models. Encouragingly, using this approach we were able to correctly predict a significant backbone conformational change of an interface loop for Target 20 (12 A rmsd between those in the unbound monomer and complex structures), but accounting for backbone flexibility in protein-protein docking is still very challenging because of the significantly larger conformational space, which must be surveyed. Motivated by these CAPRI challenges, we have made progress in reformulating RosettaDock using a "fold-tree" representation, which provides a general framework for treating a wide variety of flexible-backbone docking problems.     

Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional (3D) structure is reassessed by evaluating blind predictions, performed during 2003-2004 as part of Rounds 3-5 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Ten newly determined structures of protein-protein complexes were used as targets for these rounds. They comprised 2 enzyme-inhibitor complexes, 2 antigen-antibody complexes, 2 complexes involved in cellular signaling, 2 homo-oligomers, and a complex between 2 components of the bacterial cellulosome. For most targets, the predictors were given the experimental structures of 1 unbound and 1 bound component, with the latter in a random orientation. For some, the structure of the free component was derived from that of a related protein, requiring the use of homology modeling. In some of the targets, significant differences in conformation were displayed between the bound and unbound components, representing a major challenge for the docking procedures. For 1 target, predictions could not go to completion. In total, 1866 predictions submitted by 30 groups were evaluated. Over one-third of these groups applied completely novel docking algorithms and scoring functions, with several of them specifically addressing the challenge of dealing with side-chain and backbone flexibility. The quality of the predicted interactions was evaluated by comparison to the experimental structures of the targets, made available for the evaluation, using the well-agreed-upon criteria used previously. Twenty-four groups, which for the first time included an automatic Web server, produced predictions ranking from acceptable to highly accurate for all targets, including those where the structures of the bound and unbound forms differed substantially. These results and a brief survey of the methods used by participants of CAPRI Rounds 3-5 suggest that genuine progress in the performance of docking methods is being achieved, with CAPRI acting as the catalyst.     

Docking prediction using biological information, ZDOCK sampling technique, and clustering guided by the DFIRE statistical energy function

We entered the CAPRI experiment during the middle of Round 4 and have submitted predictions for all 6 targets released since then. We used the following procedures for docking prediction: (1) the identification of possible binding region(s) of a target based on known biological information, (2) rigid-body sampling around the binding region(s) by using the docking program ZDOCK, (3) ranking of the sampled complex conformations by employing the DFIRE-based statistical energy function, (4) clustering based on pairwise root-mean-square distance and the DFIRE energy, and (5) manual inspection and relaxation of the side-chain conformations of the top-ranked structures by geometric constraint. Reasonable predictions were made for 4 of the 6 targets. The best fraction of native contacts within the top 10 models are 89.1% for Target 12, 54.3% for Target 13, 29.3% for Target 14, and 94.1% for Target 18. The origin of successes and failures is discussed. .     

ZDOCK and RDOCK performance in CAPRI rounds 3, 4, and 5

We present an evaluation of the results of our ZDOCK and RDOCK algorithms in Rounds 3, 4, and 5 of the protein docking challenge CAPRI. ZDOCK is a Fast Fourier Transform (FFT)-based, initial-stage rigid-body docking algorithm, and RDOCK is an energy minimization algorithm for refining and reranking ZDOCK results. Of the 9 targets for which we submitted predictions, we attained at least acceptable accuracy for 7, at least medium accuracy for 6, and high accuracy for 3. These results are evidence that ZDOCK in combination with RDOCK is capable of making accurate predictions on a diverse set of protein complexes.     

Protein-protein docking with a reduced protein model accounting for side-chain flexibility

A protein-protein docking approach has been developed based on a reduced protein representation with up to three pseudo atoms per amino acid residue. Docking is performed by energy minimization in rotational and translational degrees of freedom. The reduced protein representation allows an efficient search for docking minima on the protein surfaces within. During docking, an effective energy function between pseudo atoms has been used based on amino acid size and physico-chemical character. Energy minimization of protein test complexes in the reduced representation results in geometries close to experiment with backbone root mean square deviations (RMSDs) of approximately 1 to 3 A for the mobile protein partner from the experimental geometry. For most test cases, the energy-minimized experimental structure scores among the top five energy minima in systematic docking studies when using both partners in their bound conformations. To account for side-chain conformational changes in case of using unbound protein conformations, a multicopy approach has been used to select the most favorable side-chain conformation during the docking process. The multicopy approach significantly improves the docking performance, using unbound (apo) binding partners without a significant increase in computer time. For most docking test systems using unbound partners, and without accounting for any information about the known binding geometry, a solution within approximately 2 to 3.5 A RMSD of the full mobile partner from the experimental geometry was found among the 40 top-scoring complexes. The approach could be extended to include protein loop flexibility, and might also be useful for docking of modeled protein structures.     

Performance of the first protein docking server ClusPro in CAPRI rounds 3-5

To evaluate the current status of the protein-protein docking field, the CAPRI experiment came to life. Researchers are given the receptor and ligand 3-dimensional (3D) coordinates before the cocrystallized complex is published. Human predictions of the complex structure are supposed to be submitted within 3 weeks, whereas the server ClusPro has only 24 h and does not make use of any biochemical information. From the 10 targets analyzed in the second evaluation meeting of CAPRI, ClusPro was able to predict meaningful models for 5 targets using only empirical free energy estimates. For two of the targets, the server predictions were assessed to be among the best in the field. Namely, for Targets 8 and 12, ClusPro predicted the model with the most accurate binding-site interface and the model with the highest percentage of nativelike contacts, among 180 and 230 submissions, respectively. After CAPRI, the server has been further developed to predict oligomeric assemblies, and new tools now allow the user to restrict the search for the complex to specific regions on the protein surface, significantly enhancing the predictive capabilities of the server. The performance of ClusPro in CAPRI Rounds 3-5 suggests that clustering the low free energy (i.e., desolvation and electrostatic energy) conformations of a homogeneous conformational sampling of the binding interface is a fast and reliable procedure to detect protein-protein interactions and eliminate false positives. Not including targets that had a significant structural rearrangement upon binding, the success rate of ClusPro was found to be around 71%.     

Biologically enhanced sampling geometric docking and backbone flexibility treatment with multiconformational superposition

An efficient biologically enhanced sampling geometric docking method is presented based on the FTDock algorithm to predict the protein-protein binding modes. The active site data from different sources, such as biochemical and biophysical experiments or theoretical analyses of sequence data, can be incorporated in the rotation-translation scan. When discretizing a protein onto a 3-dimensional (3D) grid, a zero value is given to grid points outside a sphere centered on the geometric center of specified residues. In this way, docking solutions are biased toward modes where the interface region is inside the sphere. We also adopt a multiconformational superposition scheme to represent backbone flexibility in the proteins. When these procedures were applied to the targets of CAPRI, a larger number of hits and smaller ligand root-mean-square deviations (RMSDs) were obtained at the conformational search stage in all cases, and especially Target 19. With Target 18, only 1 near-native structure was retained by the biologically enhanced sampling geometric docking method, but this number increased to 53 and the least ligand RMSD decreased from 8.1 A to 2.9 A after performing multiconformational superposition. These results were obtained after the CAPRI prediction deadlines.     

Empirical solvation models in the context of conformational energy searches: application to bovine pancreatic trypsin inhibitor.

Continuum solvation models that estimate free energies of solvation as a function of solvent accessible surface area are computationally simple enough to be useful for predicting protein conformation. The behavior of three such solvation models has been examined by applying them to the minimization of the conformational energy of bovine pancreatic trypsin inhibitor. The models differ only with regard to how the constants of proportionality between free energy and surface area were derived. Each model was derived by fitting to experimentally measured equilibrium solution properties. For two models, the solution property was free energy of hydration. For the third, the property was NMR coupling constants. The purpose of this study is to determine the effect of applying these solvation models to the nonequilibrium conformations of a protein arising in the course of global searches for conformational energy minima. Two approaches were used: (1) local energy minimization of an ensemble of conformations similar to the equilibrium conformation and (2) global search trajectories using Monte Carlo plus minimization starting from a single conformation similar to the equilibrium conformation. For the two models derived from free energy measurements, it was found that both the global searches and local minimizations yielded conformations more similar to the X-ray crystallographic structures than did searches or local minimizations carried out in the absence of a solvation component of the conformational energy. The model derived from NMR coupling constants behaved similarly to the other models in the context of a global search trajectory. For one of the models derived from measured free energies of hydration, it was found that minimization of an ensemble of near-equilibrium conformations yielded a new ensemble in which the conformation most similar to the X-ray determined structure PTI4 had the lowest total free energy. Despite the simplicity of the continuum solvation models, the final conformation generated in the trajectories for each of the models exhibited some of the characteristics that have been reported for conformations obtained from molecular dynamics simulations in the presence of a bath of explicit water molecules. They have smaller root mean square (rms) deviations from the experimentally determined conformation, fewer incorrect hydrogen bonds, and slightly larger radii of gyration than do conformations derived from search trajectories carried out in the absence of solvent.     

Protein-protein docking predictions for the CAPRI experiment

We predicted structures for all seven targets in the CAPRI experiment using a new method in development at the time of the challenge. The technique includes a low-resolution rigid body Monte Carlo search followed by high-resolution refinement with side-chain conformational changes and rigid body minimization. Decoys (approximately 10(6) per target) were discriminated using a scoring function including van der Waals and solvation interactions, hydrogen bonding, residue-residue pair statistics, and rotamer probabilities. Decoys were ranked, clustered, manually inspected, and selected. The top ranked model for target 6 predicted the experimental structure to 1.5 A RMSD and included 48 of 65 correct residue-residue contacts. Target 7 was predicted at 5.3 A RMSD with 22 of 37 correct residue-residue contacts using a homology model from a known complex structure. Using a preliminary version of the protocol in round 1, target 1 was predicted within 8.8 A although few contacts were correct. For targets 2 and 3, the interface locations and a small fraction of the contacts were correctly identified.     

Picosecond timescale rigid-helix and side-chain motions in deoxymyoglobin

The contribution of rigidbody motions to the atomic trajectories in a 100 ps molecular dynamics simulation of deoxymyoglobin is examined. Two typesof rigid-body motions are considered: one in which the helices are rigid units and one in which the side-chains are rigid units. Using a quaternionbased algorithm, fits of the rigid reference structures are made to each time frame of the simulation to derive trajectories of the rigid-body motions. The fitted trajectories are analysed in terms of atomic position fluctuations, mean-square displacements as a function of time, velocity autocorrelation functions and densities of states. The results are compared with the corresponding quantities calculated from the full trajectory. The relative contribution of the rigid helix motions to the helix atom dynamics depends on which quantity is examined and on which subset of atoms is chosen: rigid-helix motions contribute 86% of the rms helix backbone atomic position fluctuations, but 30% of the helix,: atom (backbone and side-chain) mean square displacements and only 1.1% of total kinetic energy. Only very low-frequency motions contribute to the rigid-helix dynamics; the rigid-body analysis allows characteristic rigid-helix vibrations to be identified and described. Treating the side-chains as rigid bodies is foundto be an excellent approximation to both their diffusive and vibrationalmean-square displacements: 96% of side-chain atom mean-square displacements originate from rigid side-Chain motions. However, the errors in theside-chain atomic positional fits are not always small. An analysis is madeof factors contributing to the positional error for different types of side-chain. © Wiley-Liss, Inc.     

Improved side-chain modeling for protein-protein docking

Success in high-resolution protein-protein docking requires accurate modeling of side-chain conformations at the interface. Most current methods either leave side chains fixed in the conformations observed in the unbound protein structures or allow the side chains to sample a set of discrete rotamer conformations. Here we describe a rapid and efficient method for sampling off-rotamer side-chain conformations by torsion space minimization during protein-protein docking starting from discrete rotamer libraries supplemented with side-chain conformations taken from the unbound structures, and show that the new method improves side-chain modeling and increases the energetic discrimination between good and bad models. Analysis of the distribution of side-chain interaction energies within and between the two protein partners shows that the new method leads to more native-like distributions of interaction energies and that the neglect of side-chain entropy produces a small but measurable increase in the number of residues whose interaction energy cannot compensate for the entropic cost of side-chain freezing at the interface. The power of the method is highlighted by a number of predictions of unprecedented accuracy in the recent CAPRI (Critical Assessment of PRedicted Interactions) blind test of protein-protein docking methods.     

Prediction of the native conformation of a polypeptide by a statistical-mechanical procedure. II. Average backbone structure of enkephalin

The average conformation of Met-enkephalin was determined by using an adaptive, importance-sampling Monte Carlo algorithm (SMAPPS—Statistical Mechanical Algorithm for Predicting Protein Structure). In the calculation, only the backbone dihedral angles (? and ψ) were allowed to vary; i.e., all side-chain (χ) and peptide-bond (ω) dihedral angles were kept fixed at the values corresponding to a low-energy structure of the pentapeptide. The total conformational energy for each randomly generated structure of the polypeptide was obtained by summing over the interaction energies of all pairs of nonbonded atoms of the whole molecule. The interaction energies were computed by the program ECEPP/2 (Empirical Conformational Energy Program for Peptides). Solvent effects were not included in the computation. The calculation was repeated until a total of 10 independent average conformations were established. The regions of conformational space occupied by the average structures were compared with the regions of low conditional free energy obtained by SMAPPS in the first paper of this series. Such a comparison provides an analysis of the capacity of SMAPPS to adjust the Monte Carlo search to regions of highest probability. The results demonstrate that the ability of SMAPPS to focus the Monte Carlo search is excellent. Finally, the 10 independent average conformations and the mean of the 10 average structures were utilized as the initial conformations for a direct energy minimization of the pentapeptide. Of the 11 final energy-minimized structures, three of the conformations were found to be equivalent to the conformation of lowest energy determined previously. In addition, all but two of the remaining energy-minimized structures were found to correspond to one of the two other conformations of high probability obtained in the first paper of this series. These results indicate that a set of independent average conformations can provide a rational, unbiased choice for the initial conformation, to be used in a direct energy minimization of a polypeptide. The final energy-minimized structures consequently constitute a set of low-energy conformations, which include the global energy minimum.     

SymmRef: a flexible refinement method for symmetric multimers

Symmetric protein complexes are abundant in the living cell. Predicting their atomic structure can shed light on the mechanism of many important biological processes. Symmetric docking methods aim to predict the structure of these complexes given the unbound structure of a single monomer, or its model. Symmetry constraints reduce the search-space of these methods and make the prediction easier compared to asymmetric protein-protein docking. However, the challenge of modeling the conformational changes that the monomer might undergo is a major obstacle. In this article, we present SymmRef, a novel method for refinement and reranking of symmetric docking solutions. The method models backbone and side-chain movements and optimizes the rigid-body orientations of the monomers. The backbone movements are modeled by normal modes minimization and the conformations of the side-chains are modeled by selecting optimal rotamers. Since solved structures of symmetric multimers show asymmetric side-chain conformations, we do not use symmetry constraints in the side-chain optimization procedure. The refined models are re-ranked according to an energy score. We tested the method on a benchmark of unbound docking challenges. The results show that the method significantly improves the accuracy and the ranking of symmetric rigid docking solutions. SymmRef is available for download at http:// bioinfo3d.cs.tau.ac.il/SymmRef/download.html.     

Protein-protein docking using 3D-Dock in rounds 3, 4, and 5 of CAPRI

In rounds 3-5 of CAPRI, the community-wide experiment on the comparative evaluation of protein-protein docking for structure prediction, we applied the 3D-Dock software package to predict the atomic structures of nine biophysical interactions. This approach starts with an initial grid-based shape complementarity search. The product of this is a large number of potential interacting conformations that are subsequently ranked by interface residue propensities and interaction energies. Refinement through detailed energetics and optimization of side-chain positions using a rotamer library is also performed. For rounds 3, 4, and 5 of the CAPRI evaluation, where possible, we clustered functional residues on the surfaces of the monomers as an indication of binding sites, using sequence based evolutionary conservations. In certain targets this provided a very useful tool for identifying the areas of interaction. During round 5, we also applied the techniques of side-chain trimming and geometrical clustering described in the literature. Of the nine target complexes in rounds 3-5, we predicted conformations that contained at least some correct contact residues for seven of these systems. For two of the targets, we submitted predictions that were considered as medium-quality. These were a nidogen-laminin complex for target 8 (T08) and a serine-threonine phosphatase bound to a targeting subunit (T14). For a further three target systems, we produced models that were rated as acceptable predictions.     

Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations

Protein-protein docking algorithms provide a means to elucidate structural details for presently unknown complexes. Here, we present and evaluate a new method to predict protein-protein complexes from the coordinates of the unbound monomer components. The method employs a low-resolution, rigid-body, Monte Carlo search followed by simultaneous optimization of backbone displacement and side-chain conformations using Monte Carlo minimization. Up to 10(5) independent simulations are carried out, and the resulting "decoys" are ranked using an energy function dominated by van der Waals interactions, an implicit solvation model, and an orientation-dependent hydrogen bonding potential. Top-ranking decoys are clustered to select the final predictions. Small-perturbation studies reveal the formation of binding funnels in 42 of 54 cases using coordinates derived from the bound complexes and in 32 of 54 cases using independently determined coordinates of one or both monomers. Experimental binding affinities correlate with the calculated score function and explain the predictive success or failure of many targets. Global searches using one or both unbound components predict at least 25% of the native residue-residue contacts in 28 of the 32 cases where binding funnels exist. The results suggest that the method may soon be useful for generating models of biologically important complexes from the structures of the isolated components, but they also highlight the challenges that must be met to achieve consistent and accurate prediction of protein-protein interactions.