@article{DurumericCharronTempletonetal.2023, author = {Durumeric, Aleksander and Charron, Nicholas and Templeton, Clark and Musil, F{\´e}lix and Bonneau, Klara and Pasos-Trejo, Aldo and Chen, Yaoyi and Kelkar, Atharva and No{\´e}, Frank and Clementi, Cecilia}, title = {Machine learned coarse-grained protein force-fields: Are we there yet?}, volume = {79}, journal = {Current Opinion in Structural Biology}, doi = {10.1016/j.sbi.2023.102533}, year = {2023}, abstract = {The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higher-body interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarse-grained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges.}, language = {en} } @article{MajewskiPerezThoelkeetal.2023, author = {Majewski, Maciej and P{\´e}rez, Adri{\`a} and Th{\"o}lke, Philipp and Doerr, Stefan and Charron, Nicholas and Giorgino, Toni and Husic, Brooke and Clementi, Cecilia and No{\´e}, Frank and De Fabritiis, Gianni}, title = {Machine learning coarse-grained potentials of protein thermodynamics}, volume = {14}, journal = {Nature Communications}, doi = {10.1038/s41467-023-41343-1}, year = {2023}, abstract = {A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.}, language = {en} }