Effective hybrid approach for protein structure prediction in a two-dimensional Hydrophobic–Polar model

Abstract

Hydrophobic–polar (HP) models are widely used to predict protein folding and hydrophobic interactions. Numerous optimization algorithms have been proposed to predict protein folding using the two-dimensional (2D) HP model. However, to obtain an optimal protein structure from the 2D HP model remains challenging. In this study, an algorithm integrating particle swarm optimization (PSO) and Tabu search (TS), named PSO–TS, was proposed to predict protein structures based on the 2D HP model. TS can help PSO to avoid getting trapped in a local optima and thus to remove the limitation of PSO in predicting protein folding by the 2D HP model. In this study, a total of 28 protein sequences were used to evaluate the accuracy of PSO–TS in protein folding prediction. The proposed PSO–TS method was compared with 15 other approaches for predicting short and long protein sequences. Experimental results demonstrated that PSO–TS provides a highly accurate, reproducible, and stabile prediction ability for the protein folding by the 2D HP model.