Solving Inverse Problems in Protein Space Using Diffusion-Based Priors
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912342143401984 |
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| author | Levy, Axel Chan, Eric R. Fridovich-Keil, Sara Poitevin, Frédéric Zhong, Ellen D. Wetzstein, Gordon |
| author_facet | Levy, Axel Chan, Eric R. Fridovich-Keil, Sara Poitevin, Frédéric Zhong, Ellen D. Wetzstein, Gordon |
| contents | The interaction of a protein with its environment can be understood and controlled via its 3D structure. Experimental methods for protein structure determination, such as X-ray crystallography or cryogenic electron microscopy, shed light on biological processes but introduce challenging inverse problems. Learning-based approaches have emerged as accurate and efficient methods to solve these inverse problems for 3D structure determination, but are specialized for a predefined type of measurement. Here, we introduce a versatile framework to turn biophysical measurements, such as cryo-EM density maps, into 3D atomic models. Our method combines a physics-based forward model of the measurement process with a pretrained generative model providing a task-agnostic, data-driven prior. Our method outperforms posterior sampling baselines on linear and non-linear inverse problems. In particular, it is the first diffusion-based method for refining atomic models from cryo-EM maps and building atomic models from sparse distance matrices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04239 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Solving Inverse Problems in Protein Space Using Diffusion-Based Priors Levy, Axel Chan, Eric R. Fridovich-Keil, Sara Poitevin, Frédéric Zhong, Ellen D. Wetzstein, Gordon Machine Learning The interaction of a protein with its environment can be understood and controlled via its 3D structure. Experimental methods for protein structure determination, such as X-ray crystallography or cryogenic electron microscopy, shed light on biological processes but introduce challenging inverse problems. Learning-based approaches have emerged as accurate and efficient methods to solve these inverse problems for 3D structure determination, but are specialized for a predefined type of measurement. Here, we introduce a versatile framework to turn biophysical measurements, such as cryo-EM density maps, into 3D atomic models. Our method combines a physics-based forward model of the measurement process with a pretrained generative model providing a task-agnostic, data-driven prior. Our method outperforms posterior sampling baselines on linear and non-linear inverse problems. In particular, it is the first diffusion-based method for refining atomic models from cryo-EM maps and building atomic models from sparse distance matrices. |
| title | Solving Inverse Problems in Protein Space Using Diffusion-Based Priors |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.04239 |