Machine learning coarse-grained potentials of protein thermodynamics

• Published in: Nature communications 2023-09, Vol.14 (1), p.5739-5739, Article 5739
• Main Authors: Majewski, Maciej, Pérez, Adrià, Thölke, Philipp, Doerr, Stefan, Charron, Nicholas E., Giorgino, Toni, Husic, Brooke E., Clementi, Cecilia, Noé, Frank, De Fabritiis, Gianni
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/2410; 631/114/663; 631/45/535/1267; 639/638/563/981; Artificial neural networks; Ataxia Telangiectasia Mutated Proteins; Biological activity; Humanities and Social Sciences; Learning algorithms; Machine Learning; Molecular dynamics; Molecular Dynamics Simulation; multidisciplinary; Neural networks; Physics; Protein structure; Proteins; Science; Science (multidisciplinary); Secondary structure; Simulation; Statistical mechanics; Structure-function relationships; Thermodynamics
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-41343-1

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. Understanding protein dynamics is a complex scientific challenge. Here, authors construct coarse-grained molecular potentials using artificial neural networks, significantly accelerating protein dynamics simulations while preserving their thermodynamics.