A unified framework for coarse grained molecular dynamics of proteins with high-fidelity reconstruction
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909421603389440 |
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| author | Zhu, Jinzhen |
| author_facet | Zhu, Jinzhen |
| contents | Simulating large proteins using traditional molecular dynamics (MD) is computationally demanding. To address this challenge, we propose a novel tree-structured coarse-grained model that efficiently captures protein dynamics. By leveraging a hierarchical protein representation, our model accurately reconstructs high-resolution protein structures, with sub-angstrom precision achieved for a 168-amino acid protein. We combine this coarse-grained model with a deep learning framework based on stochastic differential equations (SDEs). A neural network is trained to model the drift force, while a RealNVP-based noise generator approximates the stochastic component. This approach enables a significant speedup of over 20,000 times compared to traditional MD, allowing for the generation of microsecond-long trajectories within a few minutes and providing valuable insights into protein behavior. Our method demonstrates high accuracy, achieving sub-angstrom reconstruction for short (25 ns) trajectories and maintaining statistical consistency across multiple independent simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A unified framework for coarse grained molecular dynamics of proteins with high-fidelity reconstruction Zhu, Jinzhen Chemical Physics Biological Physics Biomolecules Simulating large proteins using traditional molecular dynamics (MD) is computationally demanding. To address this challenge, we propose a novel tree-structured coarse-grained model that efficiently captures protein dynamics. By leveraging a hierarchical protein representation, our model accurately reconstructs high-resolution protein structures, with sub-angstrom precision achieved for a 168-amino acid protein. We combine this coarse-grained model with a deep learning framework based on stochastic differential equations (SDEs). A neural network is trained to model the drift force, while a RealNVP-based noise generator approximates the stochastic component. This approach enables a significant speedup of over 20,000 times compared to traditional MD, allowing for the generation of microsecond-long trajectories within a few minutes and providing valuable insights into protein behavior. Our method demonstrates high accuracy, achieving sub-angstrom reconstruction for short (25 ns) trajectories and maintaining statistical consistency across multiple independent simulations. |
| title | A unified framework for coarse grained molecular dynamics of proteins with high-fidelity reconstruction |
| topic | Chemical Physics Biological Physics Biomolecules |
| url | https://arxiv.org/abs/2403.17513 |