Research Article
PIE—Efficient filters and coarse grained potentials for unbound protein–protein docking
D. V. S. Ravikant,
Ron Elber,
D. V. S. Ravikant
Department of Computer Science, Cornell University, Ithaca, New York 14853
Search for more papers by this authorCorresponding Author
Ron Elber
Department of Chemistry and Biochemistry, Institute of Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712
Department of Chemistry and Biochemistry, Institute of Computational Engineering and Sciences, University of Texas at Austin, 1 University Station, ICES, C0200, Austin, TX 78712===Search for more papers by this authorD. V. S. Ravikant,
Ron Elber,
D. V. S. Ravikant
Department of Computer Science, Cornell University, Ithaca, New York 14853
Search for more papers by this authorCorresponding Author
Ron Elber
Department of Chemistry and Biochemistry, Institute of Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712
Department of Chemistry and Biochemistry, Institute of Computational Engineering and Sciences, University of Texas at Austin, 1 University Station, ICES, C0200, Austin, TX 78712===Search for more papers by this authorAbstract
Identifying correct binding modes in a large set of models is an important step in protein–protein docking. We identified protein docking filter based on overlap area that significantly reduces the number of candidate structures that require detailed examination. We also developed potentials based on residue contacts and overlap areas using a comprehensive learning set of 640 two-chain protein complexes with mathematical programming. Our potential showed substantially better recognition capacity compared to other publicly accessible protein docking potentials in discriminating between native and nonnative binding modes on a large test set of 84 complexes independent of our training set. We were able to rank a near-native model on the top in 43 cases and within top 10 in 51 cases. We also report an atomic potential that ranks a near-native model on the top in 46 cases and within top 10 in 58 cases. Our filter+potential is well suited for selecting a small set of models to be refined to atomic resolution. Proteins 2010. © 2009 Wiley-Liss, Inc.
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