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. Proteins 2004. © 2004 Wiley‐Liss, Inc.