EncoMPASS: An encyclopedia of membrane proteins analyzed by structure and symmetry.

asymmetry biological assembly integral membrane proteins online database sequence alignment structural similarity structure alignment symmetry detection

Journal

Structure (London, England : 1993)
ISSN: 1878-4186
Titre abrégé: Structure
Pays: United States
ID NLM: 101087697

Informations de publication

Date de publication:
09 Feb 2024
Historique:
received: 24 08 2018
revised: 09 01 2024
accepted: 10 01 2024
medline: 18 2 2024
pubmed: 18 2 2024
entrez: 17 2 2024
Statut: aheadofprint

Résumé

Protein structure determination and prediction, active site detection, and protein sequence alignment techniques all exploit information about protein structure and structural relationships. For membrane proteins, however, there is limited agreement among available online tools for highlighting and mapping such structural similarities. Moreover, no available resource provides a systematic overview of quaternary and internal symmetries, and their orientation relative to the membrane, despite the fact that these properties can provide key insights into membrane protein function and evolution. Here, we describe the Encyclopedia of Membrane Proteins Analyzed by Structure and Symmetry (EncoMPASS), a database for relating integral membrane proteins of known structure from the points of view of sequence, structure, and symmetry. EncoMPASS is accessible through a web interface, and its contents can be easily downloaded. This allows the user not only to focus on specific proteins, but also to study general properties of the structure and evolution of membrane proteins.

Identifiants

pubmed: 38367624
pii: S0969-2126(24)00006-6
doi: 10.1016/j.str.2024.01.011
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

Declaration of interests The authors declare no competing interests.

Auteurs

Antoniya A Aleksandrova (AA)

Computational Structural Biology Section, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.

Edoardo Sarti (E)

Computational Structural Biology Section, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.

Lucy R Forrest (LR)

Computational Structural Biology Section, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA. Electronic address: lucy.forrest@nih.gov.

Classifications MeSH