Structure-activity relationship of presenilin in γ-secretase-mediated intramembrane cleavage.


Journal

Seminars in cell & developmental biology
ISSN: 1096-3634
Titre abrégé: Semin Cell Dev Biol
Pays: England
ID NLM: 9607332

Informations de publication

Date de publication:
09 2020
Historique:
received: 23 09 2019
revised: 18 02 2020
accepted: 19 02 2020
pubmed: 17 3 2020
medline: 25 6 2021
entrez: 16 3 2020
Statut: ppublish

Résumé

Genetic research on familial cases of Alzheimer disease have identified presenilin (PS) as an important membrane protein in the pathomechanism of this disease. PS is the catalytic subunit of γ-secretase, which is responsible for the generation of amyloid-β peptide deposited in the brains of Alzheimer disease patients. γ-Secretase is an atypical protease composed of four membrane proteins (i.e., presenilin, nicastrin, anterior pharynx defective-1 (Aph-1), and presenilin enhancer-2 (Pen-2)) and mediates intramembrane proteolysis. Numerous investigations have been conducted toward understanding the structural features of γ-secretase components as well as the cleavage mechanism of γ-secretase. In this review, we summarize our current understanding of the structure and activity relationship of the γ-secretase complex.

Identifiants

pubmed: 32171519
pii: S1084-9521(18)30291-X
doi: 10.1016/j.semcdb.2020.02.006
pii:
doi:

Substances chimiques

Membrane Proteins 0
Presenilins 0
Amyloid Precursor Protein Secretases EC 3.4.-

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

102-109

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Auteurs

Tetsuo Cai (T)

Laboratory of Neuropathology and Neuroscience, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, 113-0033, Japan.

Taisuke Tomita (T)

Laboratory of Neuropathology and Neuroscience, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, 113-0033, Japan. Electronic address: taisuke@mol.f.u-tokyo.ac.jp.

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Classifications MeSH