¹School of Computer Science, South China Normal University, Guangzhou 510631, China
 State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China
²Department of Computer Science, Sun Yat-sen University, Guangzhou 510006, China
³School of Public Health, Sun Yat-sen University, Guangzhou 510080, China

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Abstract—Using computer programs to predict protein structures from a mass of protein sequences is promising for discovering the relationship between the protein construction and their functions. In the area of computational protein structure analysis, the hydrophobicpolar (HP) model is one of the most commonly applied models. The protein folding problem based on HP model has been shown as NP-hard, to handle such an NP-hard problem, this paper proposes a discrete particle swarm optimization algorithm (DPSOHP) to solve various 2D and 3D HP lattice models-based protein folding problems. The discrete particle swarm optimization method used in DPSOHP is based on the set concept and the possibility theory from a set-based PSO (S-PSO). A selection strategy incorporating heuristic information and possibilities is adopted in DPSOHP. A particle’s positions in the algorithm are defined as a set of elements and the velocities of a particle are defined as a set of elements associated with possibilities. The experimental results on a series of 2D and 3D protein sequences show that DPSOHP is promising and performs better than various competitive state-of-the-art evolutionary algorithms.

Index Terms—Bioinformatics, Computational intelligence, Discrete particle swarm optimization, Hydrophobic-polar (HP) model, Lattice protein folding

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Cite: Jing Xiao, Liang-Ping Li, Xiao-Min Hu, "Solving Lattice Protein Folding Problems by Discrete Particle Swarm Optimization," Journal of Computers vol. 9, no. 8, pp. 1904-1913, 2014.