AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics

J Chem Theory Comput. 2024 Nov 26;20(22):9871-9878. doi: 10.1021/acs.jctc.4c01239. Epub 2024 Nov 8.

Abstract

All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities.

MeSH terms

  • Machine Learning*
  • Molecular Dynamics Simulation*
  • Neural Networks, Computer*
  • Proteins* / chemistry
  • Thermodynamics*

Substances

  • Proteins