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Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM
Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4–8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features a...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7467173/ https://www.ncbi.nlm.nih.gov/pubmed/32939280 http://dx.doi.org/10.1107/S2052252520009306 |
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author | Farrell, Daniel P. Anishchenko, Ivan Shakeel, Shabih Lauko, Anna Passmore, Lori A. Baker, David DiMaio, Frank |
author_facet | Farrell, Daniel P. Anishchenko, Ivan Shakeel, Shabih Lauko, Anna Passmore, Lori A. Baker, David DiMaio, Frank |
author_sort | Farrell, Daniel P. |
collection | PubMed |
description | Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4–8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 Å resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it comprises 6557 residues, only 1897 of which are covered by homology models. In the published model built from this map, only 387 residues could be assigned to the specific subunits with confidence. By building and placing into density 42 deep-learning-guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease-related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure. |
format | Online Article Text |
id | pubmed-7467173 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-74671732020-09-15 Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM Farrell, Daniel P. Anishchenko, Ivan Shakeel, Shabih Lauko, Anna Passmore, Lori A. Baker, David DiMaio, Frank IUCrJ Research Papers Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4–8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 Å resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it comprises 6557 residues, only 1897 of which are covered by homology models. In the published model built from this map, only 387 residues could be assigned to the specific subunits with confidence. By building and placing into density 42 deep-learning-guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease-related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure. International Union of Crystallography 2020-08-20 /pmc/articles/PMC7467173/ /pubmed/32939280 http://dx.doi.org/10.1107/S2052252520009306 Text en © Daniel P. Farrell et al. 2020 http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Research Papers Farrell, Daniel P. Anishchenko, Ivan Shakeel, Shabih Lauko, Anna Passmore, Lori A. Baker, David DiMaio, Frank Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM |
title | Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM |
title_full | Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM |
title_fullStr | Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM |
title_full_unstemmed | Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM |
title_short | Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM |
title_sort | deep learning enables the atomic structure determination of the fanconi anemia core complex from cryoem |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7467173/ https://www.ncbi.nlm.nih.gov/pubmed/32939280 http://dx.doi.org/10.1107/S2052252520009306 |
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