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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...

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Autores principales: Farrell, Daniel P., Anishchenko, Ivan, Shakeel, Shabih, Lauko, Anna, Passmore, Lori A., Baker, David, DiMaio, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2020
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.
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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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