A Knowledge Based Self-Adaptive Differential Evolution Algorithm for Protein Structure Prediction

Abstract

Tertiary protein structure prediction is one of the most challenging problems in Structural Bioinformatics, and it is a NP-Complete problem in computational complexity theory. The complexity is related to the significant number of possible conformations a single protein can assume. Metaheuristics became useful algorithms to find feasible solutions in viable computational time since exact algorithms are not capable. However, these stochastic methods are highly-dependent from parameter tuning for finding the balance between exploitation (local search refinement) and exploration (global exploratory search) capabilities. Thus, self-adaptive techniques were created to handle the parameter definition task, since it is time-consuming. In this paper, we enhance the Self-Adaptive Differential Evolution with problem-domain knowledge provided by the angle probability list approach, comparing it with every single mutation we used to compose our set of mutation operators. Moreover, a population diversity metric is used to analyze the behavior of each one of them. The proposed method was tested with ten protein sequences with different folding patterns. Results obtained showed that the self-adaptive mechanism has a better balance between the search capabilities, providing better results in regarding root mean square deviation and potential energy than the non-adaptive single-mutation methods.