AI²BMD: efficient characterization of protein dynamics withab initioaccuracy

Abstract

Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency. Classical molecular dynamics simulation is fast but lacks chemical accuracy. Quantum chemistry methods like density functional theory (DFT) can reach chemical accuracy but cannot scale to support large biomolecules. We introduce an AI-based ab initio biomolecular dynamics system (AI2BMD) that can efficiently simulate large biomolecules with ab initio accuracy. AI2BMD uses a protein fragmentation scheme and machine learning force field to achieve generalizable ab initio accuracy for energy and force calculations for various proteins comprising over 10,000 atoms. Compared to DFT, it reduces computational time by several orders of magnitude. With several hundred nanoseconds of dynamics simulations, AI2BMD demonstrated its capability of efficiently exploring the conformational space of peptides and proteins, deriving accurate 3J-couplings that match NMR experiments, and showing protein folding and unfolding tendencies. Furthermore, AI2BMD enables precise free energy calculations for protein folding, and the estimated melting temperatures are well aligned with experiments. AI2BMD could potentially complement wet-lab experiments, detect the dynamic processes of bioactivities, and enable biomedical research that is currently impossible to conduct.