2025/02/09 by Jinzhen Zhu, Zhu, Jinzhen
Biochemistry, Genetics and Molecular Biology · Chemistry · Physics and Astronomy · #Advanced NMR Techniques and Applications #Atomic Physics (physics.atom-ph) #Chemical Physics (physics.chem-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Protein Structure and Dynamics #Spectroscopy and Quantum Chemical Studies
paper · pdf · doi:10.48550/arxiv.2502.05909
openalex publication_date 2025/02/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Simulating large-scale protein dynamics using traditional all-atom molecular dynamics (MD) remains computationally prohibitive. We present a unified, universal framework for coarse-grained molecular dynamics (CG-MD) that achieves high-fidelity structural reconstruction and generalizes across diverse protein systems. Central to our approach is a hierarchical, tree-structured protein representation (TSCG) that maps Cartesian coordinates into a minimal set of interpretable collective variables. We extend this representation to accommodate multi-chain assemblies, demonstrating sub-angstrom precision in reconstructing full-atom structures from coarse-grained nodes. To model temporal evolution, we formulate protein dynamics as stochastic differential equations (SDEs), utilizing a Transformer-based architecture as a universal propagator. By representing collective variables as language-like sequences, our model transcends the limitations of protein-specific networks, generalizing to arbitrary sequence lengths and multi-chain configurations. The framework achieves an acceleration of over 10,000 to 20,000 times compared to traditional MD, generating microsecond-long trajectories within minutes. Our results show that the generated trajectories maintain statistical consistency with all-atom MD in RMSD profiles and structural ensembles. This universal model provides a salable solution for high-throughput protein simulation, offering a significant leap toward a foundation model for molecular dynamics.