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Navigating protein landscapes with a machine-learned transferable coarse-grained model

Nicholas E. Charron
Felix Musil
Andrea Guljas
Yaoyi Chen
Klara Bonneau
Aldo S. Pasos-Trejo
Jacopo Venturin
Daria Gusew
Iryna Zaporozhets
Andreas Krämer
Clark Templeton
Atharva Kelkar
Aleksander E. P. Durumeric
Simon Olsson
Adrià Pérez
Maciej Majewski
Brooke E. Husic
Ankit Patel
Gianni De Fabritiis
Frank Noé
Cecilia Clementi

October 27, 2023

The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parametrization. We demonstrate that the model successfully predicts folded structures, intermediates, metastable folded and unfolded basins, and the fluctuations of intrinsically disordered proteins while it is several orders of magnitude faster than an all-atom model. This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins.