Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

• URL: http://arxiv.org/abs/2212.07492v1
• Date: Wed, 14 Dec 2022 20:23:11 GMT
• Title: Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
• Authors: Maciej Majewski, Adri\`a P\'erez, Philipp Th\"olke, Stefan Doerr, Nicholas E. Charron, Toni Giorgino, Brooke E. Husic, Cecilia Clementi, Frank No\'e and Gianni De Fabritiis
• Abstract summary: We build coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems.
• Score: 1.3543803103181613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.

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