Protein Folding Optimization in a Hydrophobic-Polar Model for Predicting Tertiary Structure Using Fruit Fly Optimization Algorithm

Abstract

The prediction of the three-dimensional structure of a protein from its amino acid sequence is an experiment that is very much well known optimization problem which is known as the Protein Folding Optimization (PFO) in many years. The PFO problem states to the computational problem of how to predict the local structure of a protein from its amino acid. PFO problem is the NP-hard and most challenging problem. Various kind of optimization algorithm already applied for solving the PFO problem, but none of the existing algorithm not provide the accurate result within optimal time. Fruit Fly Optimization Algorithm (FOA) is a recent metaheuristics algorithm that have the intensity and diversity characteristics of searching technique. Therefore, we applied FOA for solving PFO problem in the HP (Hydrophobic-Polar) cubic lattice model. In order to increase the convergence of the FOA, we have designed and developed three different operators of FOA: smell-based search, local vision-based search and global vision-based search technique for the perspective of PFO problem. The proposed algorithm is based on two extra mechanisms centroid hydrophobic and moderator mechanism, which are accountable for improving the accomplishment of the algorithm. The centroid hydrophobic mechanism tries to move the hydrophobic monomers to the center position of the structure. The moderator mechanisms try to move a part of monomers in the protein sequence each possible directions and place at the position where the maximum energy value found. This two extra mechanisms improved the performance of the propose algorithm magically. Moreover, we have developed a reconstruction operator for producing an accurate 3D structure of protein sequences by erasing overlapping in cubic lattice points. The experiment result shows of our proposed Fruit Fly Optimization Algorithm for Protein Folding Optimization (PFO_FOA) provide better accuracy than the existing algorithms.