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3DFlex: determining structure and motion of flexible proteins from cryo-EM
Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental questions in structural biology. We introduce Three-Dimensional Flexible Refinement (3DFlex), a motion-based neural network mode...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250194/ https://www.ncbi.nlm.nih.gov/pubmed/37169929 http://dx.doi.org/10.1038/s41592-023-01853-8 |
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author | Punjani, Ali Fleet, David J. |
author_facet | Punjani, Ali Fleet, David J. |
author_sort | Punjani, Ali |
collection | PubMed |
description | Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental questions in structural biology. We introduce Three-Dimensional Flexible Refinement (3DFlex), a motion-based neural network model for continuous molecular heterogeneity for cryo-EM data. 3DFlex exploits knowledge that conformational variability of a protein is often the result of physical processes that transport density over space and tend to preserve local geometry. From two-dimensional image data, 3DFlex enables the determination of high-resolution 3D density, and provides an explicit model of a flexible protein’s motion over its conformational landscape. Experimentally, for large molecular machines (tri-snRNP spliceosome complex, translocating ribosome) and small flexible proteins (TRPV1 ion channel, αVβ8 integrin, SARS-CoV-2 spike), 3DFlex learns nonrigid molecular motions while resolving details of moving secondary structure elements. 3DFlex can improve 3D density resolution beyond the limits of existing methods because particle images contribute coherent signal over the conformational landscape. |
format | Online Article Text |
id | pubmed-10250194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-102501942023-06-10 3DFlex: determining structure and motion of flexible proteins from cryo-EM Punjani, Ali Fleet, David J. Nat Methods Article Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental questions in structural biology. We introduce Three-Dimensional Flexible Refinement (3DFlex), a motion-based neural network model for continuous molecular heterogeneity for cryo-EM data. 3DFlex exploits knowledge that conformational variability of a protein is often the result of physical processes that transport density over space and tend to preserve local geometry. From two-dimensional image data, 3DFlex enables the determination of high-resolution 3D density, and provides an explicit model of a flexible protein’s motion over its conformational landscape. Experimentally, for large molecular machines (tri-snRNP spliceosome complex, translocating ribosome) and small flexible proteins (TRPV1 ion channel, αVβ8 integrin, SARS-CoV-2 spike), 3DFlex learns nonrigid molecular motions while resolving details of moving secondary structure elements. 3DFlex can improve 3D density resolution beyond the limits of existing methods because particle images contribute coherent signal over the conformational landscape. Nature Publishing Group US 2023-05-11 2023 /pmc/articles/PMC10250194/ /pubmed/37169929 http://dx.doi.org/10.1038/s41592-023-01853-8 Text en © The Author(s) 2023 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 Punjani, Ali Fleet, David J. 3DFlex: determining structure and motion of flexible proteins from cryo-EM |
title | 3DFlex: determining structure and motion of flexible proteins from cryo-EM |
title_full | 3DFlex: determining structure and motion of flexible proteins from cryo-EM |
title_fullStr | 3DFlex: determining structure and motion of flexible proteins from cryo-EM |
title_full_unstemmed | 3DFlex: determining structure and motion of flexible proteins from cryo-EM |
title_short | 3DFlex: determining structure and motion of flexible proteins from cryo-EM |
title_sort | 3dflex: determining structure and motion of flexible proteins from cryo-em |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250194/ https://www.ncbi.nlm.nih.gov/pubmed/37169929 http://dx.doi.org/10.1038/s41592-023-01853-8 |
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