Modified Nsga-Iii As A Many-Objective Optimization Technique For Ab Initio Protein Structure Prediction - Smadi, Motasem Mohammad Yaqoob Al (2024)
Predicting the 3D structure of proteins, known as protein structure prediction (PSP), is an essential component of bioinformatics. One of the adopted approaches to solving the PSP problem is an ab initio modeling method, which has demonstrated great performance in recent years. In this work of research, the non-dominated sorting genetic algorithm III (NSGA-III) has been adapted by modeling an ab initio PSP problem as a many-objective optimization problem (MOOP). Furthermore, this study proposes four novel objectives from three distinct energy function types with the goal of solving as a MOOP. In this regard, the first two objectives are derived from the physics-based energy function (PEF) which decomposes into bond and nonbond terms. Besides, the third objective is a solvent effect model represented by SASA, and the last objective is a knowledge-based energy function (KEF) represented by dDFIRE. To demonstrate the efficiency of energy functions, the implemented NSGA-III is handled with three suggested sets of objectives: MO3SASA (consisting of bond, nonbond, and SASA); MO3dDFIRE (consisting of bond, nonbond, and dDFIRE); and MaO4 (consisting of bond, nonbond, SASA, and dDFIRE). Moreover, to further improve the exploration and exploitation of conformational space search processes with regard to NSGA-III, three genetic operators of the genetic algorithm (GA) are proposed, including two selection operators, four crossover operators, and three mutation operators. These proposed operators produced 25 conformational search algorithms (CSAs) for the proposed NSGA-III.