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.