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A unifying Bayesian framework for merging X-ray diffraction data

Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a mo...

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Autores principales: Dalton, Kevin M., Greisman, Jack B., Hekstra, Doeke R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755530/
https://www.ncbi.nlm.nih.gov/pubmed/36522310
http://dx.doi.org/10.1038/s41467-022-35280-8
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author Dalton, Kevin M.
Greisman, Jack B.
Hekstra, Doeke R.
author_facet Dalton, Kevin M.
Greisman, Jack B.
Hekstra, Doeke R.
author_sort Dalton, Kevin M.
collection PubMed
description Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.
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spelling pubmed-97555302022-12-17 A unifying Bayesian framework for merging X-ray diffraction data Dalton, Kevin M. Greisman, Jack B. Hekstra, Doeke R. Nat Commun Article Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering. Nature Publishing Group UK 2022-12-15 /pmc/articles/PMC9755530/ /pubmed/36522310 http://dx.doi.org/10.1038/s41467-022-35280-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Dalton, Kevin M.
Greisman, Jack B.
Hekstra, Doeke R.
A unifying Bayesian framework for merging X-ray diffraction data
title A unifying Bayesian framework for merging X-ray diffraction data
title_full A unifying Bayesian framework for merging X-ray diffraction data
title_fullStr A unifying Bayesian framework for merging X-ray diffraction data
title_full_unstemmed A unifying Bayesian framework for merging X-ray diffraction data
title_short A unifying Bayesian framework for merging X-ray diffraction data
title_sort unifying bayesian framework for merging x-ray diffraction data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755530/
https://www.ncbi.nlm.nih.gov/pubmed/36522310
http://dx.doi.org/10.1038/s41467-022-35280-8
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