Predict Two-Dimensional Protein Folding Based on Hydrophobic-Polar Lattice Model and Chaotic Clonal Genetic Algorithm
In order to improve the performance of prediction of protein folding problem, we introduce a relatively new chaotic clonal genetic algorithm (abbreviated as CCGA) to solve the 2D hydrophobic-polar lattice model. Our algorithm combines three successful components—(i) standard genetic algorithm (SGA), (ii) clonal selection algorithm (CSA), and (iii) chaotic operator. We compared this proposed CCGA with SGA, artificial immune system (AIS), and immune genetic algorithm (IGA) for various chain lengths. It demonstrated that CCGA had better performance than other methods over large-sized protein chains.
KeywordsProtein folding Chaotic clonal genetic algorithm Clonal selection algorithm Hydrophobic-polar model Artificial immune system
This paper was supported by Natural Science Foundation of Jiangsu Province (BK20150983), Program of Natural Science Research of Jiangsu Higher Education Institutions (15KJB470010), Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080), Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1616).
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