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Statistical mechanics of protein folding with separable energy functions
Author(s) -
Wang Jianyong,
Crippen Gordon M.
Publication year - 2004
Publication title -
biopolymers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 125
eISSN - 1097-0282
pISSN - 0006-3525
DOI - 10.1002/bip.20077
Subject(s) - partition function (quantum field theory) , chemistry , statistical mechanics , protein folding , folding (dsp implementation) , statistical potential , statistical physics , native state , chain (unit) , separable space , function (biology) , similarity (geometry) , sequence (biology) , polypeptide chain , energy (signal processing) , fold (higher order function) , biological system , protein structure prediction , protein structure , physics , crystallography , amino acid , quantum mechanics , mathematical analysis , mathematics , computer science , biochemistry , artificial intelligence , image (mathematics) , engineering , biology , evolutionary biology , programming language , electrical engineering
We have initiated an entirely new approach to statistical mechanical models of strongly interacting systems where the configurational parameters and the potential energy function are both constructed so that the canonical partition function can be evaluated analytically. For a simplified model of proteins consisting of a single, fairly short polypeptide chain without cross‐links, we can adjust the energy parameters to favor the experimentally determined native state of seven proteins having diverse types of folds. Then 497 test proteins are predicted to have stable native folds, even though they are also structurally diverse, and 480 of them have no significant sequence similarity to any of the training proteins. © 2004 Wiley Periodicals, Inc. Biopolymers, 2004