Abstract
A hybrid combination between differential evolution and a local 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.
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Acknowledgments
This work was funded by Xunta de Galicia (“Centro singular de investigación de Galicia” accreditation 2016-2019 ED431G/01) and the European Regional Development Fund (ERDF). D. Varela grant has received financial support from the Xunta de Galicia and the European Union (European Social Fund - ESF).
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Varela, D., Santos, J. (2019). Crowding Differential Evolution for Protein Structure Prediction. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_19
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DOI: https://doi.org/10.1007/978-3-030-19651-6_19
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