A Novel Offspring Selection Strategy in GAs for Protein Structure Prediction

Abstract

In the ab initio technique, the HP lattice model is one of the most frequently used methods in protein structure prediction. Although these kinds of simplified methods could not achieve high resolution, they provided a macrocosm-optimized protein structure. The model has been employed to investigate general principles of protein folding, and plays an important role in the prediction of protein structures. In this paper, we present a novel Offspring Selection Strategy in Genetic Algorithms (GAs) for the protein structure prediction (PSP) problem that beads on 2D triangular lattice HP model. In our study, an algorithm was developed by combining a crossover based on general rotation with self-adaptive offspring selection strategy and integrating into a robust local search of general pull move and mutation of K-OPT. This method was termed in this study as Offspring Selection Strategy in Genetic Algorithms (OSSGA). The experimental results showed that OSSGA could generate the lowest free energy for 4 commonly dataset used peptides in protein structure prediction. In addition the free energy obtained from OSSGA was even lower than the past Sate of The Art methods can be applied in 2D triangular lattice PSP problem.