Exploring the conformational diversity of proteins.

G-protein coupled receptors artificial intelligence conformational dynamics machine learning molecular biophysics none protein structure prediction structural biology transmembrane protein transporters

Journal

eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614

Informations de publication

Date de publication:
21 04 2022
Historique:
entrez: 21 4 2022
pubmed: 22 4 2022
medline: 23 4 2022
Statut: epublish

Résumé

An artificial intelligence-based method can predict distinct conformational states of membrane transporters and receptors.

Identifiants

pubmed: 35443909
doi: 10.7554/eLife.78549
pii: 78549
pmc: PMC9023052
doi:
pii:

Substances chimiques

Membrane Transport Proteins 0

Types de publication

Editorial

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2022, Schlessinger and Bonomi.

Déclaration de conflit d'intérêts

AS, MB No competing interests declared

Références

Nature. 2021 Aug;596(7873):590-596
pubmed: 34293799
Proc Natl Acad Sci U S A. 2021 Sep 14;118(37):
pubmed: 34507995
Nature. 2021 Aug;596(7873):583-589
pubmed: 34265844
Acta Crystallogr D Struct Biol. 2022 Jan 1;78(Pt 1):1-13
pubmed: 34981757
Nat Commun. 2019 Jul 31;10(1):3427
pubmed: 31366933
Elife. 2022 Mar 03;11:
pubmed: 35238773
Science. 2001 Oct 5;294(5540):93-6
pubmed: 11588250

Auteurs

Avner Schlessinger (A)

Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.

Massimiliano Bonomi (M)

Department of Structural Biology and Chemistry, Institut Pasteur, Université Paris Cité, Paris, France.

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Classifications MeSH