Benchmarking and analysis of protein docking performance in Rosetta v3.2

RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 'other' complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of 'other' targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction.     

Electrostatic contributions to protein-protein interactions: fast energetic filters for docking and their physical basis

The methods of continuum electrostatics are used to calculate the binding free energies of a set of protein-protein complexes including experimentally determined structures as well as other orientations generated by a fast docking algorithm. In the native structures, charged groups that are deeply buried were often found to favor complex formation (relative to isosteric nonpolar groups), whereas in nonnative complexes generated by a geometric docking algorithm, they were equally likely to be stabilizing as destabilizing. These observations were used to design a new filter for screening docked conformations that was applied, in conjunction with a number of geometric filters that assess shape complementarity, to 15 antibody-antigen complexes and 14 enzyme-inhibitor complexes. For the bound docking problem, which is the major focus of this paper, native and near-native solutions were ranked first or second in all but two enzyme-inhibitor complexes. Less success was encountered for antibody-antigen complexes, but in all cases studied, the more complete free energy evaluation was able to identify native and near-native structures. A filter based on the enrichment of tyrosines and tryptophans in antibody binding sites was applied to the antibody-antigen complexes and resulted in a native and near-native solution being ranked first and second in all cases. A clear improvement over previously reported results was obtained for the unbound antibody-antigen examples as well. The algorithm and various filters used in this work are quite efficient and are able to reduce the number of plausible docking orientations to a size small enough so that a final more complete free energy evaluation on the reduced set becomes computationally feasible.     

ZDOCK: an initial-stage protein-docking algorithm

The development of scoring functions is of great importance to protein docking. Here we present a new scoring function for the initial stage of unbound docking. It combines our recently developed pairwise shape complementarity with desolvation and electrostatics. We compare this scoring function with three other functions on a large benchmark of 49 nonredundant test cases and show its superior performance, especially for the antibody-antigen category of test cases. For 44 test cases (90% of the benchmark), we can retain at least one near-native structure within the top 2000 predictions at the 6 degrees rotational sampling density, with an average of 52 near-native structures per test case. The remaining five difficult test cases can be explained by a combination of poor binding affinity, large backbone conformational changes, and our algorithm's strong tendency for identifying large concave binding pockets. All four scoring functions have been integrated into our Fast Fourier Transform based docking algorithm ZDOCK, which is freely available to academic users at http://zlab.bu.edu/~ rong/dock.     

Protein-protein docking benchmark version 3.0

We present version 3.0 of our publicly available protein-protein docking benchmark. This update includes 40 new test cases, representing a 48% increase from Benchmark 2.0. For all of the new cases, the crystal structures of both binding partners are available. As with Benchmark 2.0, Structural Classification of Proteins (Murzin et al., J Mol Biol 1995;247:536-540) was used to remove redundant test cases. The 124 unbound-unbound test cases in Benchmark 3.0 are classified into 88 rigid-body cases, 19 medium-difficulty cases, and 17 difficult cases, based on the degree of conformational change at the interface upon complex formation. In addition to providing the community with more test cases for evaluating docking methods, the expansion of Benchmark 3.0 will facilitate the development of new algorithms that require a large number of training examples. Benchmark 3.0 is available to the public at http://zlab.bu.edu/benchmark.     

Using protein binding site prediction to improve protein docking

Predicting protein interaction interfaces and protein complexes are two important related problems. For interface prediction, there are a number of tools, such as PPI-Pred, PPISP, PINUP, Promate, and SPPIDER, which predict enzyme-inhibitor interfaces with success rates of 23% to 55% and other interfaces with 10% to 28% on a benchmark dataset of 62 complexes. Here, we develop, metaPPI, a meta server for interface prediction. It significantly improves prediction success rates to 70% for enzyme-inhibitor and 44% for other interfaces. As shown with Promate, predicted interfaces can be used to improve protein docking. Here, we follow this idea using the meta server instead of individual predictions. We confirm that filtering with predicted interfaces significantly improves candidate generation in rigid-body docking based on shape complementarity. Finally, we show that the initial ranking of candidate solutions in rigid-body docking can be further improved for the class of enzyme-inhibitor complexes by a geometrical scoring which rewards deep pockets. A web server of metaPPI is available at scoppi.tu-dresden.de/metappi. The source code of our docking algorithm BDOCK is also available at www.biotec.tu-dresden.de/~bhuang/bdock.     

A protein-RNA docking benchmark (II): extended set from experimental and homology modeling data

We present here an extended protein-RNA docking benchmark composed of 71 test cases in which the coordinates of the interacting protein and RNA molecules are available from experimental structures, plus an additional set of 35 cases in which at least one of the interacting subunits is modeled by homology. All cases in the experimental set have available unbound protein structure, and include five cases with available unbound RNA structure, four cases with a pseudo-unbound RNA structure, and 62 cases with the bound RNA form. The additional set of modeling cases comprises five unbound-model, eight model-unbound, 19 model-bound, and three model-model protein-RNA cases. The benchmark covers all major functional categories and contains cases with different degrees of difficulty for docking, as far as protein and RNA flexibility is concerned. The main objective of this benchmark is to foster the development of protein-RNA docking algorithms and to contribute to the better understanding and prediction of protein-RNA interactions. The benchmark is freely available at http://life.bsc.es/pid/protein-rna-benchmark.     

Refinement of unbound protein docking studies using biological knowledge

In this work we present two methods for the reranking of protein-protein docking studies. One scoring method searches the InterDom database for domains that are available in the proteins to be docked and evaluates the interaction of these domains in other complexes of known structure. The second one analyzes the interface of each proposed conformation with regard to the conservation of Phe, Met, and Trp and their polar neighbor residues. The special relevance of these residues is based on a publication by Ma et al. (Proc Natl Acad Sci USA 2003;100:5772-5777), who compared the conservation of all residues in the interface region to the conservation on the rest of the protein's surface. The scoring functions were tested on 30 unbound docking test cases. The evaluation of the methods is based on the ability to rerank the output of a Fast Fourier Transformation (FFT) docking. Both were able to improve the ranking of the docking output. The best improvement was achieved for enzyme-inhibitor examples. Especially the domain-based scoring function was successful and able to place a near-native solution on one of the first six ranks for 13 of 17 (76%) enzyme-inhibitor complexes [in 53% (nine complexes) even on the first rank]. The method evaluating residue conservation allowed us to increase the number of good solutions within the first 100 ranks out of approximately 9000 in 82% of the 17 enzyme-inhibitor test cases, and for seven (41%) out of 17 enzyme-inhibitor complexes, a near native solution was placed within the first seven ranks.     

Protein docking using spherical polar Fourier correlations

We present a new computational method of docking pairs of proteins by using spherical polar Fourier correlations to accelerate the search for candidate low-energy conformations. Interaction energies are estimated using a hydrophobic excluded volume model derived from the notion of "overlapping surface skins," augmented by a rigorous but "soft" model of electrostatic complementarity. This approach has several advantages over former three-dimensional grid-based fast Fourier transform (FFT) docking correlation methods even though there is no analogue to the FFT in a spherical polar representation. For example, a complete search over all six rigid-body degrees of freedom can be performed by rotating and translating only the initial expansion coefficients, many unfeasible orientations may be eliminated rapidly using only low-resolution terms, and the correlations are easily localized around known binding epitopes when this knowledge is available. Typical execution times on a single processor workstation range from 2 hours for a global search (5 x 10(8) trial orientations) to a few minutes for a local search (over 6 x 10(7) orientations). The method is illustrated with several domain dimer and enzyme-inhibitor complexes and 20 large antibody-antigen complexes, using both the bound and (when available) unbound subunits. The correct conformation of the complex is frequently identified when docking bound subunits, and a good docking orientation is ranked within the top 20 in 11 out of 18 cases when starting from unbound subunits. Proteins 2000;39:178-194.     

A non‐redundant protein–RNA docking benchmark version 2.0

We present an updated version of the protein–RNA docking benchmark, which we first published four years back. The non‐redundant protein–RNA docking benchmark version 2.0 consists of 126 test cases, a threefold increase in number compared to its previous version. The present version consists of 21 unbound–unbound cases, of which, in 12 cases, the unbound RNAs are taken from another complex. It also consists of 95 unbound–bound cases where only the protein is available in the unbound state. Besides, we introduce 10 new bound–unbound cases where only the RNA is found in the unbound state. Based on the degree of conformational change of the interface residues upon complex formation the benchmark is classified into 72 rigid‐body cases, 25 semiflexible cases and 19 full flexible cases. It also covers a wide range of conformational flexibility including small side chain movement to large domain swapping in protein structures as well as flipping and restacking in RNA bases. This benchmark should provide the docking community with more test cases for evaluating rigid‐body as well as flexible docking algorithms. Besides, it will also facilitate the development of new algorithms that require large number of training set. The protein–RNA docking benchmark version 2.0 can be freely downloaded from http://www.csb.iitkgp.ernet.in/applications/PRDBv2 . Proteins 2017; 85:256–267. © 2016 Wiley Periodicals, Inc.     

A protein-DNA docking benchmark

We present a protein-DNA docking benchmark containing 47 unbound-unbound test cases of which 13 are classified as easy, 22 as intermediate and 12 as difficult cases. The latter shows considerable structural rearrangement upon complex formation. DNA-specific modifications such as flipped out bases and base modifications are included. The benchmark covers all major groups of DNA-binding proteins according to the classification of Luscombe et al., except for the zipper-type group. The variety in test cases make this non-redundant benchmark a useful tool for comparison and development of protein-DNA docking methods. The benchmark is freely available as download from the internet.     

Score_set: A CAPRI benchmark for scoring protein complexes

Critical Assessment of PRedicted Interactions (CAPRI) has proven to be a catalyst for the development of docking algorithms. An essential step in docking is the scoring of predicted binding modes in order to identify stable complexes. In 2005, CAPRI introduced the scoring experiment, where upon completion of a prediction round, a larger set of models predicted by different groups and comprising both correct and incorrect binding modes, is made available to all participants for testing new scoring functions independently from docking calculations. Here we present an expanded benchmark data set for testing scoring functions, which comprises the consolidated ensemble of predicted complexes made available in the CAPRI scoring experiment since its inception. This consolidated scoring benchmark contains predicted complexes for 15 published CAPRI targets. These targets were subjected to 23 CAPRI assessments, due to existence of multiple binding modes for some targets. The benchmark contains more than 19,000 protein complexes. About 10% of the complexes represent docking predictions of acceptable quality or better, the remainder represent incorrect solutions (decoys). The benchmark set contains models predicted by 47 different predictor groups including web servers, which use different docking and scoring procedures, and is arguably as diverse as one may expect, representing the state of the art in protein docking. The data set is publicly available at the following URL: http://cb.iri.univ‐lille1.fr/Users/lensink/Score_set . Proteins 2014; 82:3163–3169. © 2014 Wiley Periodicals, Inc.     

PIPER: an FFT-based protein docking program with pairwise potentials

The Fast Fourier Transform (FFT) correlation approach to protein-protein docking can evaluate the energies of billions of docked conformations on a grid if the energy is described in the form of a correlation function. Here, this restriction is removed, and the approach is efficiently used with pairwise interaction potentials that substantially improve the docking results. The basic idea is approximating the interaction matrix by its eigenvectors corresponding to the few dominant eigenvalues, resulting in an energy expression written as the sum of a few correlation functions, and solving the problem by repeated FFT calculations. In addition to describing how the method is implemented, we present a novel class of structure-based pairwise intermolecular potentials. The DARS (Decoys As the Reference State) potentials are extracted from structures of protein-protein complexes and use large sets of docked conformations as decoys to derive atom pair distributions in the reference state. The current version of the DARS potential works well for enzyme-inhibitor complexes. With the new FFT-based program, DARS provides much better docking results than the earlier approaches, in many cases generating 50% more near-native docked conformations. Although the potential is far from optimal for antibody-antigen pairs, the results are still slightly better than those given by an earlier FFT method. The docking program PIPER is freely available for noncommercial applications.     

Use of pair potentials across protein interfaces in screening predicted docked complexes.

Empirical residue-residue pair potentials are used to screen possible complexes for protein-protein dockings. A correct docking is defined as a complex with not more than 2.5 A root-mean-square distance from the known experimental structure. The complexes were generated by "ftdock" (Gabb et al. J Mol Biol 1997;272:106-120) that ranks using shape complementarity. The complexes studied were 5 enzyme-inhibitors and 2 antibody-antigens, starting from the unbound crystallographic coordinates, with a further 2 antibody-antigens where the antibody was from the bound crystallographic complex. The pair potential functions tested were derived both from observed intramolecular pairings in a database of nonhomologous protein domains, and from observed intermolecular pairings across the interfaces in sets of nonhomologous heterodimers and homodimers. Out of various alternate strategies, we found the optimal method used a mole-fraction calculated random model from the intramolecular pairings. For all the systems, a correct docking was placed within the top 12% of the pair potential score ranked complexes. A combined strategy was developed that incorporated "multidock," a side-chain refinement algorithm (Jackson et al. J Mol Biol 1998;276:265-285). This placed a correct docking within the top 5 complexes for enzyme-inhibitor systems, and within the top 40 complexes for antibody-antigen systems.     

SnugDock: Paratope Structural Optimization during Antibody-Antigen Docking Compensates for Errors in Antibody Homology Models

High resolution structures of antibody-antigen complexes are useful for analyzing the binding interface and to make rational choices for antibody engineering. When a crystallographic structure of a complex is unavailable, the structure must be predicted using computational tools. In this work, we illustrate a novel approach, named SnugDock, to predict high-resolution antibody-antigen complex structures by simultaneously structurally optimizing the antibody-antigen rigid-body positions, the relative orientation of the antibody light and heavy chains, and the conformations of the six complementarity determining region loops. This approach is especially useful when the crystal structure of the antibody is not available, requiring allowances for inaccuracies in an antibody homology model which would otherwise frustrate rigid-backbone docking predictions. Local docking using SnugDock with the lowest-energy RosettaAntibody homology model produced more accurate predictions than standard rigid-body docking. SnugDock can be combined with ensemble docking to mimic conformer selection and induced fit resulting in increased sampling of diverse antibody conformations. The combined algorithm produced four medium (Critical Assessment of PRediction of Interactions-CAPRI rating) and seven acceptable lowest-interface-energy predictions in a test set of fifteen complexes. Structural analysis shows that diverse paratope conformations are sampled, but docked paratope backbones are not necessarily closer to the crystal structure conformations than the starting homology models. The accuracy of SnugDock predictions suggests a new genre of general docking algorithms with flexible binding interfaces targeted towards making homology models useful for further high-resolution predictions.     

Setting up a large set of protein-ligand PDB complexes for the development and validation of knowledge-based docking algorithms

Background  

The number of algorithms available to predict ligand-protein interactions is large and ever-increasing. The number of test cases used to validate these methods is usually small and problem dependent. Recently, several databases have been released for further understanding of protein-ligand interactions, having the Protein Data Bank as backend support. Nevertheless, it appears to be difficult to test docking methods on a large variety of complexes. In this paper we report the development of a new database of protein-ligand complexes tailored for testing of docking algorithms.     

Protein-Protein Docking Benchmark 2.0: an update

We present a new version of the Protein-Protein Docking Benchmark, reconstructed from the bottom up to include more complexes, particularly focusing on more unbound-unbound test cases. SCOP (Structural Classification of Proteins) was used to assess redundancy between the complexes in this version. The new benchmark consists of 72 unbound-unbound cases, with 52 rigid-body cases, 13 medium-difficulty cases, and 7 high-difficulty cases with substantial conformational change. In addition, we retained 12 antibody-antigen test cases with the antibody structure in the bound form. The new benchmark provides a platform for evaluating the progress of docking methods on a wide variety of targets. The new version of the benchmark is available to the public at http://zlab.bu.edu/benchmark2.     

Database driven test case generation for protein-protein docking

SUMMARY: We present a method for automatic test case generation for protein-protein docking. A consensus-type approach is proposed processing the whole PDB and classifying protein structures into complexes and unbound proteins by combining information from three different approaches (current PDB-at-a-glance classification, search of complexes by sequence identical unbound structures and chain naming). Out of this classification test cases are generated automatically. All calculations were run on the database. The information stored is available via a web interface. The user can choose several criteria for generating his own subset out of our test cases, e.g. for testing docking algorithms. AVAILABILITY: http://bibiserv.techfak.uni-bielefeld.de/agt-sdp/ CONTACT: fzoellne@techfak.uni-bielefeld.de.     

Blind docking of pharmaceutically relevant compounds using RosettaLigand

It is difficult to properly validate algorithms that dock a small molecule ligand into its protein receptor using data from the public domain: the predictions are not blind because the correct binding mode is already known, and public test cases may not be representative of compounds of interest such as drug leads. Here, we use private data from a real drug discovery program to carry out a blind evaluation of the RosettaLigand docking methodology and find that its performance is on average comparable with that of the best commercially available current small molecule docking programs. The strength of RosettaLigand is the use of the Rosetta sampling methodology to simultaneously optimize protein sidechain, protein backbone and ligand degrees of freedom; the extensive benchmark test described here identifies shortcomings in other aspects of the protocol and suggests clear routes to improving the method.     

A protein-RNA docking benchmark (I): nonredundant cases

We have developed a nonredundant protein-RNA docking benchmark dataset, which is derived from the available bound and unbound structures in the Protein Data Bank involving polypeptide and nucleic acid chains. It consists of nine unbound-unbound cases where both the protein and the RNA are available in the free form. The other 36 cases are of unbound-bound type where only the protein is available in the free form. The conformational change upon complex formation is calculated by a distance matrix alignment method, and based on that, complexes are classified into rigid, semi-flexible, and full flexible. Although in the rigid body category, no significant conformational change accompanies complex formation, the fully flexible test cases show large domain movements, RNA base flips, etc. The benchmark covers four major groups of RNA, namely, t-RNA, ribosomal RNA, duplex RNA, and single-stranded RNA. We find that RNA is generally more flexible than the protein in the complexes, and the interface region is as flexible as the molecule as a whole. The structural diversity of the complexes in the benchmark set should provide a common ground for the development and comparison of the protein-RNA docking methods. The benchmark can be freely downloaded from the internet.     

FireDock: fast interaction refinement in molecular docking

Here, we present FireDock, an efficient method for the refinement and rescoring of rigid-body docking solutions. The refinement process consists of two main steps: (1) rearrangement of the interface side-chains and (2) adjustment of the relative orientation of the molecules. Our method accounts for the observation that most interface residues that are important in recognition and binding do not change their conformation significantly upon complexation. Allowing full side-chain flexibility, a common procedure in refinement methods, often causes excessive conformational changes. These changes may distort preformed structural signatures, which have been shown to be important for binding recognition. Here, we restrict side-chain movements, and thus manage to reduce the false-positive rate noticeably. In the later stages of our procedure (orientation adjustments and scoring), we smooth the atomic radii. This allows for the minor backbone and side-chain movements and increases the sensitivity of our algorithm. FireDock succeeds in ranking a near-native structure within the top 15 predictions for 83% of the 30 enzyme-inhibitor test cases, and for 78% of the 18 semiunbound antibody-antigen complexes. Our refinement procedure significantly improves the ranking of the rigid-body PatchDock algorithm for these cases. The FireDock program is fully automated. In particular, to our knowledge, FireDock's prediction results are comparable to current state-of-the-art refinement methods while its running time is significantly lower. The method is available at http://bioinfo3d.cs.tau.ac.il/FireDock/.