Significance of conformational biases in Monte Carlo simulations of protein folding: lessons from Metropolis-Hastings approach.
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.
Przytycka
Teresa
T
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, Maryland 20894, USA. przytyck@ncbi.nlm.nih.bov
D003198:Computer Simulation / Q000639:trends*; D009010:Monte Carlo Method; D011487:Protein Conformation; D017510:Protein Folding; D020836:Protein Structure, Quaternary; D013816:Thermodynamics