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.

蛋白质折叠是蛋白质折叠成其最终天然结构的动态过程。这与预测最终蛋白质结构的传统问题不同，因为它需要对蛋白质成分如何随时间相互作用进行建模以获得最终折叠结构。在这项研究中，我们测试是否可以仅通过机器学习获得折叠过程的模型。为此，蛋白质折叠被认为是一个紧急过程，并使用元胞自动机工具对折叠过程进行建模。定义了神经元胞自动机，使用连接主义模型通过蛋白质链充当元胞自动机来定义动态折叠。差分进化用于自动获得提供蛋白质折叠的优化神经元胞自动机。