A multi-population memetic algorithm for the 3-D protein structure prediction problem

摘要

In this paper, we present a knowledge-based memetic algorithm to tackle the three-dimensional protein structure prediction problem without the explicit use of experimentally determined protein structures' templates. Our algorithm proposal was divided into two main prediction steps: (i) solutions sampling and initialization; and (ii) structural models' optimization coming from the previous stage. The first step generates and classifies several structural models for a given target protein, through the Angle Probability List strategy, to identify distinct structural patterns and consider reasonable solutions in the memetic algorithm initialization. The Angle Probability List takes advantage of structural knowledge stored in the Protein Data Bank to reduce the size and, consequently, the conformational search space complexity. The second step of the method consists in the optimization of the structures generated in the first stage by the proposed memetic algorithm. It uses a tree-based population where each node can be seen as an independent subpopulation that interacts with each other over global search operations, aiming at knowledge sharing, population diversity, and better exploration of the multimodal search space. The method also encompasses ad hoc global search operators, whose objective is to increase the method exploration ability focusing on specific characteristics of the protein structure prediction problem, combined with the artificial bee colony algorithm used as an exploitation technique applied to each node of the tree. The proposed algorithm was tested on a set of 24 amino acid sequences, as well as compared to the reference method in the protein structure prediction area, the method of Rosetta. The obtained results show the ability of our method to predict three-dimensional protein structures with similar folding to the experimentally determined ones, regarding the structural metrics Root-Mean-Square Deviation and Global Distance Total Score Test. We also show that our method was able to reach comparable results to Rosetta, and in some cases, it outperformed Rosetta, corroborating the effectiveness of our proposal.