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

• URL: http://arxiv.org/abs/2310.18278v1
• Date: Fri, 27 Oct 2023 17:10:23 GMT
• Title: Navigating protein landscapes with a machine-learned transferable coarse-grained model
• Authors: Nicholas E. Charron, Felix Musil, Andrea Guljas, Yaoyi Chen, Klara Bonneau, Aldo S. Pasos-Trejo, Jacopo Venturin, Daria Gusew, Iryna Zaporozhets, Andreas Kr\"amer, Clark Templeton, Atharva Kelkar, Aleksander E. P. Durumeric, Simon Olsson, Adri\`a P\'erez, Maciej Majewski, Brooke E. Husic, Ankit Patel, Gianni De Fabritiis, Frank No\'e, Cecilia Clementi
• Abstract summary: coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. We develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences. We demonstrate that the model successfully predicts folded structures, intermediates, metastable folded and unfolded basins, and the fluctuations of intrinsically disordered proteins.
• Score: 29.252004942896875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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