Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2502.05909 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915938224308224 |
|---|---|
| author | Zhu, Jinzhen |
| author_facet | Zhu, Jinzhen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05909 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards a Universal Foundation Model for Protein Dynamics: A Multi-Chain Tree-Structured Framework with Transformer Propagators Zhu, Jinzhen Atomic Physics Chemical Physics Computational Physics 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. |
| title | Towards a Universal Foundation Model for Protein Dynamics: A Multi-Chain Tree-Structured Framework with Transformer Propagators |
| topic | Atomic Physics Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2502.05909 |