Protein structure prediction with energy minimization and deep learning approaches

Juan Luis Filgueiras; Daniel Varela; José Santos

doi:10.1007/s11047-023-09943-4

Protein structure prediction with energy minimization and deep learning approaches

Nat Comput. 2023 May 8:1-12. doi: 10.1007/s11047-023-09943-4. Online ahead of print.

Authors

Juan Luis Filgueiras¹, Daniel Varela¹, José Santos¹

Affiliation

¹ Department of Computer Science and Information Technologies, CITIC (Centre for Information and Communications Technology Research), University of A Coruña, A Coruña, Spain.

Abstract

In this paper we discuss the advantages and problems of two alternatives for ab initio protein structure prediction. On one hand, recent approaches based on deep learning, which have significantly improved prediction results for a wide variety of proteins, are discussed. On the other hand, methods based on protein conformational energy minimization and with different search strategies are analyzed. In this latter case, our methods based on a memetic combination between differential evolution and the fragment replacement technique are included, incorporating also the possibility of niching in the evolutionary search. Different proteins have been used to analyze the pros and cons in both approaches, proposing possibilities of integration of both alternatives.

Keywords: Crowding niching method; Deep learning; Differential evolution; Evolutionary computing niching methods; Protein structure prediction.