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Significance of conformational biases in Monte Carlo simulations of protein folding: Lessons from Metropolis–Hastings approach
Author(s) -
Przytycka Teresa
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.20210
Subject(s) - monte carlo method , markov chain monte carlo , metropolis–hastings algorithm , statistical physics , computer science , detailed balance , markov chain , protein folding , hybrid monte carlo , algorithm , mathematics , chemistry , physics , machine learning , statistics , biochemistry
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. Proteins 2004. © 2004 Wiley‐Liss, Inc.