Literature DB >> 23996272

How good is automated protein docking?

Dima Kozakov1, Dmitri Beglov, Tanggis Bohnuud, Scott E Mottarella, Bing Xia, David R Hall, Sandor Vajda.   

Abstract

The protein docking server ClusPro has been participating in critical assessment of prediction of interactions (CAPRI) since its introduction in 2004. This article evaluates the performance of ClusPro 2.0 for targets 46-58 in Rounds 22-27 of CAPRI. The analysis leads to a number of important observations. First, ClusPro reliably yields acceptable or medium accuracy models for targets of moderate difficulty that have also been successfully predicted by other groups, and fails only for targets that have few acceptable models submitted. Second, the quality of automated docking by ClusPro is very close to that of the best human predictor groups, including our own submissions. This is very important, because servers have to submit results within 48 h and the predictions should be reproducible, whereas human predictors have several weeks and can use any type of information. Third, while we refined the ClusPro results for manual submission by running computationally costly Monte Carlo minimization simulations, we observed significant improvement in accuracy only for two of the six complexes correctly predicted by ClusPro. Fourth, new developments, not seen in previous rounds of CAPRI, are that the top ranked model provided by ClusPro was acceptable or better quality for all these six targets, and that the top ranked model was also the highest quality for five of the six, confirming that ranking models based on cluster size can reliably identify the best near-native conformations.
Copyright © 2013 Wiley Periodicals, Inc.

Entities:  

Keywords:  CAPRI docking experiment; method development; protein-protein docking; structure refinement; user community; web-based server

Mesh:

Substances:

Year:  2013        PMID: 23996272      PMCID: PMC3934018          DOI: 10.1002/prot.24403

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  39 in total

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Authors:  Stephen R Comeau; David W Gatchell; Sandor Vajda; Carlos J Camacho
Journal:  Bioinformatics       Date:  2004-01-01       Impact factor: 6.937

2.  ZDOCK: an initial-stage protein-docking algorithm.

Authors:  Rong Chen; Li Li; Zhiping Weng
Journal:  Proteins       Date:  2003-07-01

3.  CAPRI: a Critical Assessment of PRedicted Interactions.

Authors:  Joël Janin; Kim Henrick; John Moult; Lynn Ten Eyck; Michael J E Sternberg; Sandor Vajda; Ilya Vakser; Shoshana J Wodak
Journal:  Proteins       Date:  2003-07-01

4.  A protein-protein docking benchmark.

Authors:  Rong Chen; Julian Mintseris; Joël Janin; Zhiping Weng
Journal:  Proteins       Date:  2003-07-01

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

Authors:  Jeffrey J Gray; Stewart Moughon; Chu Wang; Ora Schueler-Furman; Brian Kuhlman; Carol A Rohl; David Baker
Journal:  J Mol Biol       Date:  2003-08-01       Impact factor: 5.469

6.  ClusPro: a fully automated algorithm for protein-protein docking.

Authors:  Stephen R Comeau; David W Gatchell; Sandor Vajda; Carlos J Camacho
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

7.  Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques.

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Authors:  D Shortle; K T Simons; D Baker
Journal:  Proc Natl Acad Sci U S A       Date:  1998-09-15       Impact factor: 11.205

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Authors:  I A Vakser
Journal:  Biopolymers       Date:  1996-09       Impact factor: 2.505

10.  Modelling protein docking using shape complementarity, electrostatics and biochemical information.

Authors:  H A Gabb; R M Jackson; M J Sternberg
Journal:  J Mol Biol       Date:  1997-09-12       Impact factor: 5.469

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