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Predicting large‐scale conformational changes in proteins using energy‐weighted normal modes
Author(s) -
Palmer David S.,
Jensen Frank
Publication year - 2011
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.23105
Subject(s) - molecular dynamics , cartesian coordinate system , principal component analysis , biological system , granularity , normal mode , protein structure , computer science , chemistry , statistical physics , computational chemistry , physics , mathematics , biology , biochemistry , artificial intelligence , geometry , quantum mechanics , vibration , operating system
We report the development of a method to improve the sampling of protein conformational space in molecular simulations. It is shown that a principal component analysis of energy‐weighted normal modes in Cartesian coordinates can be used to extract vectors suitable for describing the dynamics of protein substructures. The method can operate with either atomistic or user‐defined coarse‐grained models of protein structure. An implicit reverse coarse‐graining allows the dynamics of all‐atoms to be recovered when a coarse‐grained model is used. For an external test set of four proteins, it is shown that the new method is more successful than normal mode analysis in describing the large‐scale conformational changes observed on ligand binding. The method has potential applications in protein–ligand and protein–protein docking and in biasing molecular dynamics simulations. Proteins 2011; © 2011 Wiley‐Liss, Inc.