Performance of ZDOCK and IRAD in CAPRI rounds 39-45.
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ABSTRACT:
SUBMITTER: Vreven T
PROVIDER: S-EPMC7374054 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We report the performance of our protein-protein docking pipeline, including the ZDOCK rigid-body docking algorithm, on 19 targets in CAPRI rounds 28-34. Following the docking step, we reranked the ZDOCK predictions using the IRAD scoring function, pruned redundant predictions, performed energy landscape analysis, and utilized our interface prediction approach RCF. In addition, we applied constraints to the search space based on biological information that we culled from the literature, which increased the chance of making a correct prediction. For all but two targets we were able to find and apply biological information and we found the information to be highly accurate, indicating that effective incorporation of biological information is an important component for protein-protein docking. Proteins 2017; 85:408-416. © 2016 Wiley Periodicals, Inc.
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Project description:We report the performance of the ZDOCK and ZRANK algorithms in CAPRI rounds 13-19 and introduce a novel measure atom contact frequency (ACF). To compute ACF, we identify the residues that most often make contact with the binding partner in the complete set of ZDOCK predictions for each target. We used ACF to predict the interface of the proteins, which, in combination with the biological data available in the literature, is a valuable addition to our docking pipeline. Furthermore, we incorporated a straightforward and efficient clustering algorithm with two purposes: (1) to determine clusters of similar docking poses (corresponding to energy funnels) and (2) to remove redundancies from the final set of predictions. With these new developments, we achieved at least one acceptable prediction for targets 29 and 36, at least one medium-quality prediction for targets 41 and 42, and at least one high-quality prediction for targets 37 and 40; thus, we succeeded for six out of a total of 12 targets.
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Project description:CAPRI challenges offer a variety of blind tests for protein-protein interaction prediction. In CAPRI Rounds 38-45, we generated a set of putative binding modes for each target with an FFT-based docking algorithm, and then scored and ranked these binding modes with a proprietary scoring function, ITScorePP. We have also developed a novel web server, Rebipp. The algorithm utilizes information retrieval to identify relevant biological information to significantly reduce the search space for a particular protein. In parallel, we have also constructed a GPU-based docking server, MDockPP, for protein-protein complex structure prediction. Here, the performance of our protocol in CAPRI rounds 38-45 is reported, which include 16 docking and scoring targets. Among them, three targets contain multiple interfaces: Targets 124, 125, and 136 have 2, 4, and 3 interfaces, respectively. In the predictor experiments, we predicted correct binding modes for nine targets, including one high-accuracy interface, six medium-accuracy binding modes, and six acceptable-accuracy binding modes. For the docking server prediction experiments, we predicted correct binding modes for eight targets, including one high-accuracy, three medium-accuracy, and five acceptable-accuracy binding modes.
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Project description:Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8. In addition, we were able to generate the only medium-accuracy models for a particularly challenging target, T121. In contrast to the classical peptide-mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta-sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide-protein interactions, we extracted PeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta-sheet complementation, and tested our protocol for global peptide-docking PIPER-FlexPepDock on this dataset. We find that the beta-strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.
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Project description:Modeling the conformational changes that occur on binding of macromolecules is an unsolved challenge. In previous rounds of the Critical Assessment of PRediction of Interactions (CAPRI), it was demonstrated that the Rosetta approach to macromolecular modeling could capture side chain conformational changes on binding with high accuracy. In rounds 13-19 we tested the ability of various backbone remodeling strategies to capture the main-chain conformational changes observed during binding events. These approaches span a wide range of backbone motions, from limited refinement of loops to relieve clashes in homologous docking, through extensive remodeling of loop segments, to large-scale remodeling of RNA. Although the results are encouraging, major improvements in sampling and energy evaluation are clearly required for consistent high accuracy modeling. Analysis of our failures in the CAPRI challenges suggest that conformational sampling at the termini of exposed beta strands is a particularly pressing area for improvement.
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Project description:Critical Assessment of PRediction of Interactions (CAPRI) rounds 37 through 45 introduced larger complexes, new macromolecules, and multistage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pregenerated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in round 41. Although the results were broadly encouraging, they also highlighted the pressing need to invest in (a) flexible docking algorithms with the ability to model loop and linker motions and in (b) new sampling and scoring methods for oligosaccharide-protein interactions.
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Project description:In CAPRI rounds 13-19, we submitted models that are of acceptable or higher quality for 6 of the total of 13 targets. This success builds on our record in previous CAPRI rounds. The docking problem can be divided into two steps. In the first, translational/rotational and conformational space is searched to generate a pool of docked poses; the success of this search step is measured by whether near-native poses are included in the pool. In the second step, the pool is selected for near-native poses. In our previous assessment of CAPRI results, we suggested that the search problem is largely solved; a remaining problem is to select near-native poses. Our work in these new rounds of CAPRI was guided by this assessment. To solve the selection problem, we used an assortment of criteria on the interfaces of candidate poses. In one extreme, represented by T29, with very little known interface information, our criterion for top models was based on interface prediction. Poses in which the predicted interface residues occurred in interfaces were selected. Our model 1 for T29 was of medium quality. In the other extreme, represented by T40, with reliably known interface information, our selection was solely based on such information. Nine of the ten models submitted for T40 were of high (3 models), medium (4 models), and acceptable (2 models) quality. Our strategy of mixing predicted and known interface information appears to be widely applicable for the selection of near-native poses.
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