LMProt: An Efficient Algorithm for Monte Carlo Sampling of Protein Conformational Space

Abstract

A new and efficient Monte Carlo algorithm for sampling protein configurations in the continuous space is presented; the efficiency of this algorithm, named Local Moves for Proteins (LMProt), was compared to other alternative algorithms. For this purpose, we used an intrachain interaction energy function that is proportional to the root mean square deviation ( rmsd) with respect to α-carbons from native structures of real proteins. For phantom chains, the LMProt method is ∼10 4 and 20 times faster than the algorithms Thrashing (no local moves) and Sevenfold Way (local moves), respectively. Additionally, the LMProt was tested for real chains (excluded-volume all-atoms model); proteins 5NLL (138 residues) and 1BFF (129 residues) were used to determine the folding success ξ as a function of the number η of residues involved in the chain movements, and as a function of the maximum amplitude of atomic displacement δr max. Our results indicate that multiple local moves associated with relative chain flexibility, controlled by appropriate adjustments for η and δr max, are essential for configurational search efficiency.