Protein Structure Prediction Using A New Optimization-Based Evolutionary and Explainable Artificial Intelligence Approach

Abstract

Protein structure prediction (PSP) is an important scientific problem because it helps humans to understand how proteins perform their biological functions. This paper models the PSP problem as a multi-objective optimization problem with three fast and accurate knowledge-based energy functions. This way, using evolutionary computation (EC)-based artificial intelligence (AI) approach to solve this multi-objective PSP problem to find the optimal structure is explainable. Considering that the multiple populations for multiple objectives (MPMO) framework shows efficient performance in solving lots of multi-objective benchmarks and real-world problems, this paper proposes a new AI approach named improved MPMO-based differential evolution (IMPMO-DE) to solve the multi-objective PSP problem. To our best knowledge, this is the first time that MPMO is applied to PSP, with three novel strategies. First, an adaptive archive-based mutation strategy is proposed to better balance the exploration and exploitation abilities by adaptively using different archive-based mutation operators in different evolutionary stages. Second, a mixed individual transfer strategy is proposed to share search information among the multiple populations to accelerate the convergence speed. Third, an evolvable archive update strategy is proposed to generate more promising solutions through evolving the archived solutions. IMPMO-DE is tested on 28 representative proteins and all the available template-free modeling proteins up to 404 residues in the famous Critical Assessment of Protein Structure Prediction (CASP14) competition. Experimental results show that IMPMO-DE performs better than the compared state-of-the-art EC-based PSP methods and ranks above average compared with all the CASP14 competitors. More importantly, IMPMO-DE is a new efficient AI approach that opens a promising optimization-based evolutionary and explainable way for efficient PSP rather than deep learning approaches like AlphaFold2, especially for newly discovered proteins without similar known protein structures.