Efficient Conformational Search Method for Peptides and Proteins: Monte Carlo Minimization with an Adaptive Bias

Abstract

The energy function of a protein consists of a tremendous number of minima. Locating the global energy minimum (GEM), which corresponds to the native structure, is a severe problem in global optimization. The commonly used Monte Carlo minimization (MCM) method is based on a random selection of torsional angle values. We suggest selecting these values with biased probabilities depending on the increased structure−energy correlations as the GEM is approached during the search. Our method applied to models of the 5-residue peptide Leu-enkephalin finds the GEM ∼2.7 faster than MCM.