Optimization of protein folding using chemical reaction optimization in HP cubic lattice model

Protein folding optimization is a very important and tough problem in computational biology. For solving this problem, a population-based metaheuristic algorithm named chemical reaction optimization (CRO) with HP cubic lattice model has been proposed in this paper. The proposed algorithm is combined with evolution and H&P compliance mechanisms which are responsible for increasing the performance of the algorithm. The evolution mechanism improves the performance of each individual solution. On the other hand, the H&P compliance mechanism tries to place the H monomer close to the center and place the P monomer as far as possible from the center of the related structure. The algorithm also applies four reactant operations of typical CRO algorithm decomposition, on-wall ineffective collision, synthesis and inter-molecular ineffective collision to solve the problem efficiently. The reactants or mechanisms may cause overlapping of the corresponding solutions. The algorithm also includes a repair mechanism which transforms invalid solutions into valid ones by removing overlapping in cubic lattice points. This algorithm has been tested over some sets of sequences and it shows very good performance.