Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM.

Fanconi anemia core complex cryoEM deep learning distance predictions

Journal

IUCrJ
ISSN: 2052-2525
Titre abrégé: IUCrJ
Pays: England
ID NLM: 101623101

Informations de publication

Date de publication:
01 Sep 2020
Historique:
received: 07 04 2020
accepted: 07 07 2020
entrez: 17 9 2020
pubmed: 18 9 2020
medline: 18 9 2020
Statut: epublish

Résumé

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

Identifiants

pubmed: 32939280
doi: 10.1107/S2052252520009306
pii: eh5009
pmc: PMC7467173
doi:

Types de publication

Journal Article

Langues

eng

Pagination

881-892

Subventions

Organisme : Medical Research Council
ID : MC_U105192715
Pays : United Kingdom
Organisme : NIGMS NIH HHS
ID : R01 GM123089
Pays : United States

Informations de copyright

© Daniel P. Farrell et al. 2020.

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Auteurs

Daniel P Farrell (DP)

Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.

Ivan Anishchenko (I)

Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.

Shabih Shakeel (S)

MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.

Anna Lauko (A)

Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.

Lori A Passmore (LA)

MRC Laboratory of Molecular Biology, Cambridge, United Kingdom.

David Baker (D)

Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.

Frank DiMaio (F)

Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
Institute for Protein Design, University of Washington, Seattle, WA 98105, USA.

Classifications MeSH