Glutton: a tool for generating structural ensembles of partly disordered proteins from chemical shifts.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 04 2019
01 04 2019
Historique:
received:
19
03
2018
revised:
15
07
2018
accepted:
03
09
2018
pubmed:
6
9
2018
medline:
13
2
2020
entrez:
6
9
2018
Statut:
ppublish
Résumé
Many proteins are partially disordered in physiological conditions and only fold, fully or partially, upon binding. Their structural analysis is challenging because the accessible information, typically chemical shifts (CS) from nuclear magnetic resonance experiments, are averages over broad ensembles of conformations. We aim to develop a database for the analysis of such data in terms of conformational distributions of the protein backbone rather than of individual high-resolution structures. Glutton is the largest available database linking CS and protein 3D structures (5270 entries organized in three levels) and is searchable via a python script. It generates statistical distributions of ϕ-ψ dihedral angles based on CS or vice versa. Such ϕ-ψ distributions are used to calculate structural ensembles of partially disordered proteins from their CS. For folded proteins, such ensembles are excellent starting points for further refinement with additional experimental restraints (structure determination) or computational methods (structure prediction). Glutton is freely available at https://github.com/YeeHo/Glutton. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 30184055
pii: 5090450
doi: 10.1093/bioinformatics/bty755
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1234-1236Informations de copyright
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.