Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
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.
Cite
@article{arxiv.2212.07492,
title = {Machine Learning Coarse-Grained Potentials of Protein Thermodynamics},
author = {Maciej Majewski and Adrià Pérez and Philipp Thölke and Stefan Doerr and Nicholas E. Charron and Toni Giorgino and Brooke E. Husic and Cecilia Clementi and Frank Noé and Gianni De Fabritiis},
journal= {arXiv preprint arXiv:2212.07492},
year = {2024}
}