Protein structure prediction by applying an evolutionary algorithm

Abstract

Interest in protein structure prediction is widespread, and has been previously addressed using evolutionary algorithms, such as the simple genetic algorithm (GA), messy GA (mga), fast messy GA (fmGA), and linkage learning GA (LLGA). However, past research used off the shelf software such as GENOCOP, GENESIS, and mGA. In this study we report results of a modified fmGA, which is found to be "good" at finding semi-optimal solutions in a reasonable time. Our study focuses on tuning this fmGA in an attempt to improve the effectiveness and efficiency of the algorithm in solving a protein structure and in finding better ways to identify secondary structures. Problem definition, protein model representation, mapping to algorithm domain, tool selection modifications and conducted experiments are discussed.