Crowding differential evolution for protein structure prediction

• Autores: Daniel Varela Suanzes-Carpegna, J.L Santos
• Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.), José Ramón Álvarez Sánchez (dir. congr.), Félix de la Paz López (dir. congr.), Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 193-203
• Idioma: inglés
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• Resumen

  • A hybrid combination between differential evolution and alocal refinement of protein structures provided by fragment replacements was performed for protein structure prediction. The coarse-grained protein conformation representation of the Rosetta environment was used.Given the deceptiveness of the Rosetta energy model, an evolutionary computing niching method, crowding, was incorporated in the evolutionary algorithm with the aim to obtain optimized solutions that at the same time provide a set of diverse protein folds. Thus, the probability to obtain optimized conformations close to the native structure is increased.