Diversification strategies in differential evolution algorithm to solve the protein structure prediction problem

Abstract

The protein structure prediction is considered as one of the most important open problems in biology and bioinformatics due the huge amount of plausible shapes that a protein can assume. The objective of this paper is to apply the Differential Evolution (DE) algorithm employing two simple diversification strategies known as generation gap and Gaussian perturbation to solve the protein structure prediction problem in the backbone and side-chain model. To test our approaches the 1PLW, 1ZDD and 1CRN proteins were used and the standard DE algorithm was compared with DE using the diversification approaches and with some state-of-art algorithms. Also, the genotypic diversity was analyzed during the algorithm run, showing the impacts generated by the diversification mechanisms. Despite its simplicity, the proposed approaches achieved competitive results.