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

Charron, Nicholas E.; Pérez Culubret, Adrià; Majewski, Maciej; De Fabritiis, Gianni; Clementi, Cecilia

doi:http://dx.doi.org/10.1038/s41557-025-01874-0

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

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Charron NE, Bonneau K, Pasos-Trejo AS, Guljas A, Chen Y, Musil F, et al. Navigating protein landscapes with a machine-learned transferable coarse-grained model. Nat Chem. 2025 Aug;17(8):1284-92. DOI: 10.1038/s41557-025-01874-0

Abstract

The most popular and universally predictive protein simulation models employ all-atom molecular dynamics, 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 parameterization. We demonstrate that the model successfully predicts metastable states of folded, unfolded and intermediate structures, the fluctuations of intrinsically disordered proteins and relative folding free energies of protein mutants, while being 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.