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Evolving Cellular Automata Schemes for Protein Folding Modeling Using the Rosetta Atomic Representation

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http://hdl.handle.net/2183/31017
Atribución 4.0 Internacional
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Title
Evolving Cellular Automata Schemes for Protein Folding Modeling Using the Rosetta Atomic Representation
Author(s)
Varela, Daniel
Santos Reyes, José
Date
2022
Citation
Varela, D., Santos, J. Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representation. Genet Program Evolvable Mach 23, 225–252 (2022). https://doi.org/10.1007/s10710-022-09427-x
Abstract
[Abstract] Protein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to automatically obtain the optimized neural cellular automata that provide protein folding. We tested the methods with the Rosetta coarse-grained atomic model of protein representation, using different proteins to analyze the modeling of folding and the structure refinement that the modeling can provide, showing the potential advantages that such methods offer, but also difficulties that arise.
Keywords
Protein folding
Neural cellular automata
Differential evolution
 
Description
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG 
Editor version
https://doi.org/10.1007/s10710-022-09427-x
Rights
Atribución 4.0 Internacional

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