Monte Carlo simulation of proteins through a random walk in energy space

Abstract

A Monte Carlo algorithm that performs a random walk in energy space has been used to study random coil–helix and random coil–beta sheet transitions in model proteins. This method permits estimation of the density of states of a protein via a random walk on the energy surface, thereby allowing the system to escape from local free-energy minima with relative ease. A cubic lattice model and a knowledge based force field are employed for these simulations. It is shown that, for a given amino acid sequence, the method is able to fold long polypeptides reproducibly. Its results compare favorably with those of annealing and parallel tempering simulations, which have been used before in the same context. This method is used to examine the effect of amino acid sequence and chain length on the folding of several designer polypeptides.