Repository: Freie Universität Berlin, Math Department

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

Majewski, Maciej and Pérez, Adrià and Thölke, Philipp and Doerr, Stefan and Charron, Nicholas E. and Giorgino, Toni and Husic, Brooke E. and Clementi, Cecilia and Noé, Frank and De Fabritiis, Gianni (2023) Machine Learning Coarse-Grained Potentials of Protein Thermodynamics. Nature Communications, 14 .

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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.

Item Type:Article
Subjects:Mathematical and Computer Sciences
Mathematical and Computer Sciences > Mathematics
Mathematical and Computer Sciences > Mathematics > Applied Mathematics
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics
ID Code:2950
Deposited By: Monika Drueck
Deposited On:20 Apr 2023 07:50
Last Modified:05 Feb 2024 10:58

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