Protein Tertiary Structure Prediction using a β-Hill Climbing Optimizer

Abstract

Predicting the protein tertiary structure from its amino acids is a big challenge nowadays. There are huge number of protein sequences with unknown structures. However, there are many computational methods which tried to solve this problem, in general, these methods achieve good results for small size proteins, but most of them were not powerful when the conformational space is huge. Many studies have tried to solve this problem using either local search algorithms or global search algorithms; whereas some studies have used hybrid algorithms which make use of local search-based algorithms and population-based meta-heuristic algorithms. This study introduces a new algorithm, β- hill climbing Algorithm, to solve the ab initio protein tertiary structure prediction problem. The β- hill climbing Algorithm is used to find the local optimal solution within the search space by its Iterated Local Search (ILS). Furthermore. The proposed algorithm predicts the tertiary structure of a protein without any prior knowledge but based on its sequence alone. The proposed algorithm has been evaluated using two protein sequences; Met-enkephalin (1PLW) and Plant Seed Protein (1CRN). The results show that the proposed algorithm can find more good solutions compare to other previous studies.