Abstract
We have modeled the protein folding process with cellular automata using the Face-Centered Cubic lattice model. An artificial neural network implements a cellular automaton-like scheme that defines the moves of each of the amino acids of the protein chain and through several time iterations until a folded protein is obtained. Differential Evolution was used to evolve these neural cellular automata, which take the information for defining the folding process from the energy space considered in the lattice model. Different proteins were used for testing the process, comparing the results of the folded structures against other methods of direct prediction of the final folded conformation.
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Acknowledgments
This work was funded by the Ministry of Economy and Competitiveness of Spain (project TIN2013-40981-R) and Xunta de Galicia (project GPC ED431B 2016/035).
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Varela, D., Santos, J. (2017). Protein Folding Modeling with Neural Cellular Automata Using the Face-Centered Cubic Model. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_13
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