Generative modeling of protein ensembles guided by crystallographic electron densities
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913616989519872 |
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| author | Maddipatla, Sai Advaith Sellam, Nadav Bojan Vedula, Sanketh Marx, Ailie Bronstein, Alex |
| author_facet | Maddipatla, Sai Advaith Sellam, Nadav Bojan Vedula, Sanketh Marx, Ailie Bronstein, Alex |
| contents | Proteins are dynamic, adopting ensembles of conformations. The nature of this conformational heterogenity is imprinted in the raw electron density measurements obtained from X-ray crystallography experiments. Fitting an ensemble of protein structures to these measurements is a challenging, ill-posed inverse problem. We propose a non-i.i.d. ensemble guidance approach to solve this problem using existing protein structure generative models and demonstrate that it accurately recovers complicated multi-modal alternate protein backbone conformations observed in certain single crystal measurements. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_13223 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative modeling of protein ensembles guided by crystallographic electron densities Maddipatla, Sai Advaith Sellam, Nadav Bojan Vedula, Sanketh Marx, Ailie Bronstein, Alex Quantitative Methods Artificial Intelligence Machine Learning Proteins are dynamic, adopting ensembles of conformations. The nature of this conformational heterogenity is imprinted in the raw electron density measurements obtained from X-ray crystallography experiments. Fitting an ensemble of protein structures to these measurements is a challenging, ill-posed inverse problem. We propose a non-i.i.d. ensemble guidance approach to solve this problem using existing protein structure generative models and demonstrate that it accurately recovers complicated multi-modal alternate protein backbone conformations observed in certain single crystal measurements. |
| title | Generative modeling of protein ensembles guided by crystallographic electron densities |
| topic | Quantitative Methods Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.13223 |