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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.00013 |
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| _version_ | 1866918152201306112 |
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| author | Liu, Yikai Zheng, Haoyang Mao, Lining Wang, Yanbin Chen, Ming Lin, Guang |
| author_facet | Liu, Yikai Zheng, Haoyang Mao, Lining Wang, Yanbin Chen, Ming Lin, Guang |
| contents | Molecular dynamics (MD) simulation has long been the principal computational tool for exploring protein conformational landscapes and dynamics, but its application is limited by high computational cost. We present ProTDyn, a foundation protein language model that unifies conformational ensemble generation and multi-timescale dynamics modeling within a single framework. Unlike prior approaches that treat these tasks separately, ProTDyn allows flexible independent and identically distributed (i.i.d.) ensemble sampling and dynamic trajectory simulation. Across diverse protein systems, ProTDyn yields thermodynamically consistent ensembles, faithfully reproduces dynamical properties over multiple timescales, and generalizes to proteins beyond its training data. It offers a scalable and efficient alternative to conventional MD simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ProTDyn: a foundation Protein language model for Thermodynamics and Dynamics generation Liu, Yikai Zheng, Haoyang Mao, Lining Wang, Yanbin Chen, Ming Lin, Guang Biological Physics Molecular dynamics (MD) simulation has long been the principal computational tool for exploring protein conformational landscapes and dynamics, but its application is limited by high computational cost. We present ProTDyn, a foundation protein language model that unifies conformational ensemble generation and multi-timescale dynamics modeling within a single framework. Unlike prior approaches that treat these tasks separately, ProTDyn allows flexible independent and identically distributed (i.i.d.) ensemble sampling and dynamic trajectory simulation. Across diverse protein systems, ProTDyn yields thermodynamically consistent ensembles, faithfully reproduces dynamical properties over multiple timescales, and generalizes to proteins beyond its training data. It offers a scalable and efficient alternative to conventional MD simulations. |
| title | ProTDyn: a foundation Protein language model for Thermodynamics and Dynamics generation |
| topic | Biological Physics |
| url | https://arxiv.org/abs/2510.00013 |