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.
The heavily used protein–protein docking server ClusPro performs three computational steps as follows: (1) rigid body docking, (2) RMSD based clustering of the 1000 lowest energy structures, and (3) the removal of steric clashes by energy minimization. In response to challenges encountered in recent CAPRI targets, we added three new options to ClusPro. These are (1) accounting for small angle X‐ray scattering data in docking; (2) considering pairwise interaction data as restraints; and (3) enabling discrimination between biological and crystallographic dimers. In addition, we have developed an extremely fast docking algorithm based on 5D rotational manifold FFT, and an algorithm for docking flexible peptides that include known sequence motifs. We feel that these developments will further improve the utility of ClusPro. However, CAPRI emphasized several shortcomings of the current server, including the problem of selecting the right energy parameters among the five options provided, and the problem of selecting the best models among the 10 generated for each parameter set. In addition, results convinced us that further development is needed for docking homology models. Finally, we discuss the difficulties we have encountered when attempting to develop a refinement algorithm that would be computationally efficient enough for inclusion in a heavily used server. Proteins 2017; 85:435–444. © 2016 Wiley Periodicals, Inc.
more » « less- NSF-PAR ID:
- 10037075
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Proteins: Structure, Function, and Bioinformatics
- Volume:
- 85
- Issue:
- 3
- ISSN:
- 0887-3585
- Page Range / eLocation ID:
- p. 435-444
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Summary We present an approach for the efficient docking of peptide motifs to their free receptor structures. Using a motif based search, we can retrieve structural fragments from the Protein Data Bank (PDB) that are very similar to the peptide’s final, bound conformation. We use a Fast Fourier Transform (FFT) based docking method to quickly perform global rigid body docking of these fragments to the receptor. According to CAPRI peptide docking criteria, an acceptable conformation can often be found among the top-ranking predictions.
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