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Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly
Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279371/ https://www.ncbi.nlm.nih.gov/pubmed/35831370 http://dx.doi.org/10.1038/s41467-022-31748-9 |
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author | He, Jiahua Lin, Peicong Chen, Ji Cao, Hong Huang, Sheng-You |
author_facet | He, Jiahua Lin, Peicong Chen, Ji Cao, Hong Huang, Sheng-You |
author_sort | He, Jiahua |
collection | PubMed |
description | Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building method of multi-chain protein complexes from intermediate-resolution cryo-EM maps, named EMBuild, by integrating AlphaFold structure prediction, FFT-based global fitting, domain-based semi-flexible refinement, and graph-based iterative assembling on the main-chain probability map predicted by a deep convolutional network. EMBuild is extensively evaluated on diverse test sets of 47 single-particle EM maps at 4.0–8.0 Å resolution and 16 subtomogram averaging maps of cryo-ET data at 3.7–9.3 Å resolution, and compared with state-of-the-art approaches. We demonstrate that EMBuild is able to build high-quality complex structures that are comparably accurate to the manually built PDB structures from the cryo-EM maps. These results demonstrate the accuracy and reliability of EMBuild in automatic model building. |
format | Online Article Text |
id | pubmed-9279371 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92793712022-07-15 Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly He, Jiahua Lin, Peicong Chen, Ji Cao, Hong Huang, Sheng-You Nat Commun Article Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building method of multi-chain protein complexes from intermediate-resolution cryo-EM maps, named EMBuild, by integrating AlphaFold structure prediction, FFT-based global fitting, domain-based semi-flexible refinement, and graph-based iterative assembling on the main-chain probability map predicted by a deep convolutional network. EMBuild is extensively evaluated on diverse test sets of 47 single-particle EM maps at 4.0–8.0 Å resolution and 16 subtomogram averaging maps of cryo-ET data at 3.7–9.3 Å resolution, and compared with state-of-the-art approaches. We demonstrate that EMBuild is able to build high-quality complex structures that are comparably accurate to the manually built PDB structures from the cryo-EM maps. These results demonstrate the accuracy and reliability of EMBuild in automatic model building. Nature Publishing Group UK 2022-07-13 /pmc/articles/PMC9279371/ /pubmed/35831370 http://dx.doi.org/10.1038/s41467-022-31748-9 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 He, Jiahua Lin, Peicong Chen, Ji Cao, Hong Huang, Sheng-You Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly |
title | Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly |
title_full | Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly |
title_fullStr | Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly |
title_full_unstemmed | Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly |
title_short | Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly |
title_sort | model building of protein complexes from intermediate-resolution cryo-em maps with deep learning-guided automatic assembly |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279371/ https://www.ncbi.nlm.nih.gov/pubmed/35831370 http://dx.doi.org/10.1038/s41467-022-31748-9 |
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