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T. Przytycka’s Research Group

Teresa M. Przytycka’s research group

Algorithmic and Graph Theoretical methods in

Computational and Systems Biology

Monte Carlo simulation of protein folding

Significance of conformational biases in Monte Carlo simulations of protein folding: lessons from Metropolis-Hastings approach

Teresa M. Przytycka

Proteins. 2004 Nov 1;57(2):338-44. Pubmed

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.