JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908606395318272 |
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| author | Daigavane, Ameya Vani, Bodhi P. Davidson, Darcy Saremi, Saeed Rackers, Joshua Kleinhenz, Joseph |
| author_facet | Daigavane, Ameya Vani, Bodhi P. Davidson, Darcy Saremi, Saeed Rackers, Joshua Kleinhenz, Joseph |
| contents | Conformational ensembles of protein structures are immensely important both for understanding protein function and drug discovery in novel modalities such as cryptic pockets. Current techniques for sampling ensembles such as molecular dynamics (MD) are computationally inefficient, while many recent machine learning methods do not transfer to systems outside their training data. We propose JAMUN which performs MD in a smoothed, noised space of all-atom 3D conformations of molecules by utilizing the framework of walk-jump sampling. JAMUN enables ensemble generation for small peptides at rates of an order of magnitude faster than traditional molecular dynamics. The physical priors in JAMUN enables transferability to systems outside of its training data, even to peptides that are longer than those originally trained on. Our model, code and weights are available at https://github.com/prescient-design/jamun. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles Daigavane, Ameya Vani, Bodhi P. Davidson, Darcy Saremi, Saeed Rackers, Joshua Kleinhenz, Joseph Biological Physics Machine Learning Biomolecules Conformational ensembles of protein structures are immensely important both for understanding protein function and drug discovery in novel modalities such as cryptic pockets. Current techniques for sampling ensembles such as molecular dynamics (MD) are computationally inefficient, while many recent machine learning methods do not transfer to systems outside their training data. We propose JAMUN which performs MD in a smoothed, noised space of all-atom 3D conformations of molecules by utilizing the framework of walk-jump sampling. JAMUN enables ensemble generation for small peptides at rates of an order of magnitude faster than traditional molecular dynamics. The physical priors in JAMUN enables transferability to systems outside of its training data, even to peptides that are longer than those originally trained on. Our model, code and weights are available at https://github.com/prescient-design/jamun. |
| title | JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles |
| topic | Biological Physics Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2410.14621 |