Protein structure prediction with energy minimization and deep learning approaches

Filgueiras Rilo, Juan LuisVarela, DanielSantos Reyes, José2024-06-262024-06-262023-12J. L.Filgueiras, D. Varela & J. Santos, "Protein structure prediction with energy minimization and deep learning approaches", Natural Computing, Vol. 22, Issue 4, pp. 659 - 670, Dec. 2023, doi: 10.1007/s11047-023-09943-41567-7818http://hdl.handle.net/2183/37418Financiado para publicación en acceso aberto: CRUE-CSIC/Springer Nature[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.engAttribution 4.0 International Licensehttp://creativecommons.org/licenses/by/3.0/es/Crowding niching methodDeep learningDifferential evolutionEvolutionary computing niching methodsProtein structure predictionProtein structure prediction with energy minimization and deep learning approachesjournal articleopen access10.1007/s11047-023-09943-4