Genetic Algorithm Embedded with a Search Space Dimension Reduction Scheme for Efficient Peptide Structure Predictions.

Abstract

The computational determination of peptide conformations is a challenging task of finding minima in a high dimensional space. By combining the sampling efficiency of the genetic algorithm (GA) and the dimensionality reduction resulted from the backbone dihedral angle correlations, named as the path matrix (PM) method, a new searching algorithm, parallel microgenetic algorithm (PMGA), is proposed. Meanwhile, PMGA employs the density functional theory based energy as the fitness function and performs local geometry optimizations to enhance the reliability of its GA encoding strategy. Tests on peptides with up to eight amino-acid residues show PMGA is quite efficient for providing high-quality conformational coverages. The computational cost of the PMGA search increases slowly with the number of amino-acid residues in a peptide, with no sign of deterioration on the searching results for the increased length of the peptide. The PMGA method should therefore be useful for determining the conformations of oligopeptide, studying the protein-ligand interactions, and designing the peptide-based drugs.