Cargando…
OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis
Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analys...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630141/ https://www.ncbi.nlm.nih.gov/pubmed/37813988 http://dx.doi.org/10.1038/s41592-023-02031-6 |
_version_ | 1785132093150330880 |
---|---|
author | Luo, Zhenwei Ni, Fengyun Wang, Qinghua Ma, Jianpeng |
author_facet | Luo, Zhenwei Ni, Fengyun Wang, Qinghua Ma, Jianpeng |
author_sort | Luo, Zhenwei |
collection | PubMed |
description | Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analysis often yields static structures. Here we describe OPUS-DSD, an algorithm capable of efficiently reconstructing the structural landscape embedded in cryo-EM data. OPUS-DSD uses a three-dimensional convolutional encoder–decoder architecture trained with cryo-EM images, thereby encoding structural variations into a smooth and easily analyzable low-dimension space. This space can be traversed to reconstruct continuous dynamics or clustered to identify distinct conformations. OPUS-DSD can offer meaningful insights into the structural variations of macromolecules, filling in the gaps left by traditional cryo-EM structural determination, and potentially improves the reconstruction resolution by reliably clustering similar particles within the dataset. These functionalities are especially relevant to the study of highly dynamic biological systems. OPUS-DSD is available at https://github.com/alncat/opusDSD. |
format | Online Article Text |
id | pubmed-10630141 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-106301412023-11-09 OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis Luo, Zhenwei Ni, Fengyun Wang, Qinghua Ma, Jianpeng Nat Methods Article Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analysis often yields static structures. Here we describe OPUS-DSD, an algorithm capable of efficiently reconstructing the structural landscape embedded in cryo-EM data. OPUS-DSD uses a three-dimensional convolutional encoder–decoder architecture trained with cryo-EM images, thereby encoding structural variations into a smooth and easily analyzable low-dimension space. This space can be traversed to reconstruct continuous dynamics or clustered to identify distinct conformations. OPUS-DSD can offer meaningful insights into the structural variations of macromolecules, filling in the gaps left by traditional cryo-EM structural determination, and potentially improves the reconstruction resolution by reliably clustering similar particles within the dataset. These functionalities are especially relevant to the study of highly dynamic biological systems. OPUS-DSD is available at https://github.com/alncat/opusDSD. Nature Publishing Group US 2023-10-09 2023 /pmc/articles/PMC10630141/ /pubmed/37813988 http://dx.doi.org/10.1038/s41592-023-02031-6 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 Luo, Zhenwei Ni, Fengyun Wang, Qinghua Ma, Jianpeng OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis |
title | OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis |
title_full | OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis |
title_fullStr | OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis |
title_full_unstemmed | OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis |
title_short | OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis |
title_sort | opus-dsd: deep structural disentanglement for cryo-em single-particle analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630141/ https://www.ncbi.nlm.nih.gov/pubmed/37813988 http://dx.doi.org/10.1038/s41592-023-02031-6 |
work_keys_str_mv | AT luozhenwei opusdsddeepstructuraldisentanglementforcryoemsingleparticleanalysis AT nifengyun opusdsddeepstructuraldisentanglementforcryoemsingleparticleanalysis AT wangqinghua opusdsddeepstructuraldisentanglementforcryoemsingleparticleanalysis AT majianpeng opusdsddeepstructuraldisentanglementforcryoemsingleparticleanalysis |