A Multi-objective Approach to the Protein Structure Prediction Problem using the Biased Random-Key Genetic Algorithm

Proteins are base molecules present in live organisms. The study of their structures and functions is of considerable importance for many application fields, particularly for the pharmaceutical area. This paper presents a multi-objective model to the Protein Structure Prediction problem, using three objectives: energy score, secondary structure information, and contact maps information. A BRKGA method is used as a global optimizer and adapted to work with multiple objective problems. Also, the MUFOLD-CL clustering method is applied to select a single predicted structure. Experiments were carried out to analyze the proposed model performance using state-of-the-art ab initio algorithms for comparison. Results obtained indicate that the proposed approach is competitive in terms of RMSD and GDT metrics.

I. Introduction

It is well-known that proteins’ spatial structure determines many of their essential biological functions [1]. Among possible applications involving protein structure prediction, it is possible to highlight drugs’ synthesis for specific uses and an in-depth analysis of diseases and their possible treatments.