Repository: Freie Universität Berlin, Math Department

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

Charron, Nicholas E. and Musil, Felix and Guljas, Andrea and Chen, Yaoyi and Bonneau, Klara and Pasos-Trejo, Aldo S. and Venturin, Jacopo and Gusew, Daria and Zaporozhets, Iryna and Krämer, Andreas and Templeton, Clark and Kelkar, Atharva and Durumeric, Alexander E.P. and Olsson, Simon and Pérez, Adrià and Majewski, Maciej and Husic, Brooke E. and Patel, Ankit and Fabritiis, Gianni De and Noé, Frank and Clementi, Cecilia (2023) Navigating protein landscapes with a machine-learned transferable coarse-grained model. Preprint . (Unpublished)

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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.

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:3070
Deposited By: Jana Jerosch
Deposited On:01 Feb 2024 12:41
Last Modified:01 Feb 2024 12:41

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