期刊
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
卷 57, 期 2, 页码 338-344出版社
WILEY
DOI: 10.1002/prot.20210
关键词
protein folding; Metropolis-Hasting algorithm; detailed balance principle; biased sampling; hierarchical folding; minimalist models
Despite significant effort, the problem of predicting a protein's three-dimensional fold from its amino-acid sequence remains unsolved. An important strategy involves treating folding as a statistical process, using the Markov chain formalism, implemented as a Metropolis Monte Carlo algorithm. A formal prerequisite of this approach is the condition of detailed balance, the plausible requirement that at equilibrium, the transition from state i to state j is traversed with the same probability as the reverse transition from state j to state i. Surprisingly, some relatively successful methods that use biased sampling fail to satisfy this requirement. Is this compromise merely a convenient heuristic that results in faster convergence? Or, is it instead a cryptic energy term that compensates for an incomplete potential function? I explore this question using Metropolis-Hasting Monte Carlo simulations. Results from these simulations suggest the latter answer is more likely. (C) 2004 Wiley-Liss, Inc.
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