Literature DB >> 17918726

Docking and scoring protein complexes: CAPRI 3rd Edition.

Marc F Lensink1, Raúl Méndez, Shoshana J Wodak.   

Abstract

The performance of methods for predicting protein-protein interactions at the atomic scale is assessed by evaluating blind predictions performed during 2005-2007 as part of Rounds 6-12 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). These Rounds also included a new scoring experiment, where a larger set of models contributed by the predictors was made available to groups developing scoring functions. These groups scored the uploaded set and submitted their own best models for assessment. The structures of nine protein complexes including one homodimer were used as targets. These targets represent biologically relevant interactions involved in gene expression, signal transduction, RNA, or protein processing and membrane maintenance. For all the targets except one, predictions started from the experimentally determined structures of the free (unbound) components or from models derived by homology, making it mandatory for docking methods to model the conformational changes that often accompany association. In total, 63 groups and eight automatic servers, a substantial increase from previous years, submitted docking predictions, of which 1994 were evaluated here. Fifteen groups submitted 305 models for five targets in the scoring experiment. Assessment of the predictions reveals that 31 different groups produced models of acceptable and medium accuracy-but only one high accuracy submission-for all the targets, except the homodimer. In the latter, none of the docking procedures reproduced the large conformational adjustment required for correct assembly, underscoring yet again that handling protein flexibility remains a major challenge. In the scoring experiment, a large fraction of the groups attained the set goal of singling out the correct association modes from incorrect solutions in the limited ensembles of contributed models. But in general they seemed unable to identify the best models, indicating that current scoring methods are probably not sensitive enough. With the increased focus on protein assemblies, in particular by structural genomics efforts, the growing community of CAPRI predictors is engaged more actively than ever in the development of better scoring functions and means of modeling conformational flexibility, which hold promise for much progress in the future. (c) 2007 Wiley-Liss, Inc.

Mesh:

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Year:  2007        PMID: 17918726     DOI: 10.1002/prot.21804

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


  114 in total

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Authors:  Pierre Tuffery; Philippe Derreumaux
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4.  Computational molecular biology approaches to ligand-target interactions.

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5.  How good is automated protein docking?

Authors:  Dima Kozakov; Dmitri Beglov; Tanggis Bohnuud; Scott E Mottarella; Bing Xia; David R Hall; Sandor Vajda
Journal:  Proteins       Date:  2013-10-17

Review 6.  Convergence and combination of methods in protein-protein docking.

Authors:  Sandor Vajda; Dima Kozakov
Journal:  Curr Opin Struct Biol       Date:  2009-03-25       Impact factor: 6.809

Review 7.  Sampling and scoring: a marriage made in heaven.

Authors:  Sandor Vajda; David R Hall; Dima Kozakov
Journal:  Proteins       Date:  2013-08-19

8.  Structural templates for modeling homodimers.

Authors:  Petras J Kundrotas; Ilya A Vakser; Joël Janin
Journal:  Protein Sci       Date:  2013-09-20       Impact factor: 6.725

9.  Optimized atomic statistical potentials: assessment of protein interfaces and loops.

Authors:  Guang Qiang Dong; Hao Fan; Dina Schneidman-Duhovny; Ben Webb; Andrej Sali
Journal:  Bioinformatics       Date:  2013-09-27       Impact factor: 6.937

10.  Specific and non-specific protein association in solution: computation of solvent effects and prediction of first-encounter modes for efficient configurational bias Monte Carlo simulations.

Authors:  Antonio Cardone; Harish Pant; Sergio A Hassan
Journal:  J Phys Chem B       Date:  2013-10-07       Impact factor: 2.991

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