Charron, Nicholas E.Pérez Culubret, AdriàMajewski, MaciejDe Fabritiis, GianniClementi, Cecilia2025-09-162025-09-162025Charron 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/s41557-025-01874-01755-4330http://hdl.handle.net/10230/71199The 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.application/pdfeng© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s41557-025-01874-0Computational biophysicsComputational chemistryinfo:eu-repo/semantics/openAccess