HPS_PSP: HIGH PERFORMANCE SYSTEM FOR PROTEIN STRUCTURE PREDICTION

Prediction of least energy conformation of a protein from its primary structure (chain of amino acids) is an optimization problem associated with a large complex energy landscape. In this study, a simple 2D hydrophobic-hydrophilic model was used to model the protein sequence, which allows the fast and efficient design of genetic algorithm-based protein structure prediction approach. The neighborhood search strategy is integrated into the genetic operator. The neighborhood search guides the genetic operator to regions in the computational space with good solutions. To prevent convergence to local optima, the proposed method employs crowding-based parent replacement strategy, which improves the performance of the algorithm and the ability to deal with multiple numbers of solutions. The proposed algorithm was tested with a standard benchmark of HP sequences and comparative results demonstrate that the proposed system beats most of the evolutionary algorithms for seven sequences. It finds the best energy for a sequence of length 50 (E = - 22), 64 (E = - 42), 85 (E = - 54), 100 (E = - 71), 102 (E = - 75), 123 (E = - 91) and 136 (E = - 105).