Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why

TL;DR

This paper introduces a data-driven ab initio generalized Langevin equation (AIGLE) method for accurately modeling high-dimensional, coarse-grained conformational dynamics in molecular systems, ensuring dynamical consistency with all-atom simulations.

Contribution

It develops a novel AIGLE framework with practical criteria for long-term dynamical accuracy, applicable to complex molecular conformational dynamics.

Findings

AIGLE models are consistent with all-atom molecular dynamics.

Practical criteria improve long-term dynamical accuracy.

Case studies demonstrate effectiveness for polymers and peptides.

Abstract

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained conformational dynamics. Constrained by the fluctuation-dissipation theorem, the approach can build coarse-grained models in dynamical consistency with all-atom molecular dynamics. We also propose practical criteria for AIGLE to enforce long-term dynamical consistency. Case studies of a toy polymer, with 20 coarse-grained sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling coarse-grained conformational dynamics in practice.