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Distinguish protein decoys by Using a scoring function based on a new AMBER force field, short molecular dynamics simulations, and the generalized born solvent model
Author(s) -
Lee Mathew C.,
Duan Yong
Publication year - 2004
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.10470
Subject(s) - decoy , force field (fiction) , molecular dynamics , function (biology) , protein structure prediction , statistical physics , computer science , physics , artificial intelligence , protein structure , computational chemistry , chemistry , biology , nuclear magnetic resonance , biochemistry , evolutionary biology , receptor
Recent works have shown the ability of physics‐based potentials (e.g., CHARMM and OPLS‐AA) and energy minimization to differentiate the native protein structures from large ensemble of non‐native structures. In this study, we extended previous work by other authors and developed an energy scoring function using a new set of AMBER parameters (also recently developed in our laboratory) in conjunction with molecular dynamics and the Generalized Born solvent model. We evaluated the performance of our new scoring function by examining its ability to distinguish between the native and decoy protein structures. Here we present a systematic comparison of our results with those obtained with use of other physics‐based potentials by previous authors. A total of 7 decoy sets, 117 protein sequences, and more than 41,000 structures were evaluated. The results of our study showed that our new scoring function represents a significant improvement over previously published physics‐based scoring functions. Proteins 2004. © 2004 Wiley‐Liss, Inc.