Abstract
This article discusses the advantages and problems of different approaches to ab initio protein structure prediction. Recent successful approaches based on deep learning are compared with those based on protein fragment replacements and energy minimization with different search strategies, including ours based on evolutionary algorithms. Selected proteins are considered to analyze the approaches, focusing on the problems of those based on deep learning.
This study was funded by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC (ED431G 2019/01), GPC ED431B 2019/03 and IN845D-02 (funded by the “Agencia Gallega de Innovación”, co-financed by Feder funds, supported by the “Consellería de Economía, Empleo e Industria” of Xunta de Galicia), and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00).
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Filgueiras, J.L., Varela, D., Santos, J. (2022). Energy Minimization vs. Deep Learning Approaches for Protein Structure Prediction. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022. Lecture Notes in Computer Science, vol 13259. Springer, Cham. https://doi.org/10.1007/978-3-031-06527-9_11
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