Suppr超能文献

酶-底物杂合β-折叠控制γ-分泌酶活性位点的几何形状和水的进入。

Enzyme-substrate hybrid β-sheet controls geometry and water access to the γ-secretase active site.

机构信息

Center of Functional Protein Assemblies, Technical University of Munich, Garching, Germany.

German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.

出版信息

Commun Biol. 2023 Jun 24;6(1):670. doi: 10.1038/s42003-023-05039-y.

Abstract

γ-Secretase is an aspartyl intramembrane protease that cleaves the amyloid precursor protein (APP) involved in Alzheimer's disease pathology and other transmembrane proteins. Substrate-bound structures reveal a stable hybrid β-sheet immediately following the substrate scissile bond consisting of β1 and β2 from the enzyme and β3 from the substrate. Molecular dynamics simulations and enhanced sampling simulations demonstrate that the hybrid β-sheet stability is strongly correlated with the formation of a stable cleavage-compatible active geometry and it also controls water access to the active site. The hybrid β-sheet is only stable for substrates with 3 or more C-terminal residues beyond the scissile bond. The simulation model allowed us to predict the effect of Pro and Phe mutations that weaken the formation of the hybrid β-sheet which were confirmed by experimental testing. Our study provides a direct explanation why γ-secretase preferentially cleaves APP in steps of 3 residues and how the hybrid β-sheet facilitates γ-secretase proteolysis.

摘要

γ-分泌酶是一种天冬氨酸跨膜蛋白酶,可切割淀粉样前体蛋白(APP),该蛋白参与阿尔茨海默病的病理过程及其他跨膜蛋白。底物结合结构揭示了在底物切口键之后紧接着存在稳定的混合β-片层,其由酶的β1 和β2 以及底物的β3 组成。分子动力学模拟和增强采样模拟表明,混合β-片层的稳定性与形成稳定的可切割活性构象密切相关,它还控制着水进入活性位点。混合β-片层仅对在切口键后具有 3 个或更多 C 末端残基的底物稳定。该模拟模型使我们能够预测削弱混合β-片层形成的 Pro 和 Phe 突变的影响,实验测试证实了这一点。我们的研究提供了一个直接的解释,说明为什么 γ-分泌酶优先以 3 个残基的步长切割 APP,以及混合β-片层如何促进 γ-分泌酶蛋白水解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92fa/10290658/28ae08e0ee4f/42003_2023_5039_Fig1_HTML.jpg

相似文献

2
Recognition of the amyloid precursor protein by human γ-secretase.人γ-分泌酶对淀粉样前体蛋白的识别。
Science. 2019 Feb 15;363(6428). doi: 10.1126/science.aaw0930. Epub 2019 Jan 10.
5
Structural Modeling of γ-Secretase Aβ Complex Formation and Substrate Processing.γ-分泌酶 Aβ 复合物形成和底物加工的结构建模。
ACS Chem Neurosci. 2019 Mar 20;10(3):1826-1840. doi: 10.1021/acschemneuro.8b00725. Epub 2019 Jan 30.
10
Molecular Dynamics Activation of γ-Secretase for Cleavage of the Notch1 Substrate.分子动力学激活 γ-分泌酶以切割 Notch1 底物。
ACS Chem Neurosci. 2023 Dec 6;14(23):4216-4226. doi: 10.1021/acschemneuro.3c00594. Epub 2023 Nov 9.

本文引用的文献

1
Elucidating the Protonation State of the γ-Secretase Catalytic Dyad.阐明γ-分泌酶催化二元组的质子化状态。
ACS Chem Neurosci. 2023 Jan 18;14(2):261-269. doi: 10.1021/acschemneuro.2c00563. Epub 2022 Dec 23.
5
Mechanism of Tripeptide Trimming of Amyloid β-Peptide 49 by γ-Secretase.γ-分泌酶对淀粉样β肽 49 的三肽修剪机制。
J Am Chem Soc. 2022 Apr 13;144(14):6215-6226. doi: 10.1021/jacs.1c10533. Epub 2022 Apr 4.
6
Lipid21: Complex Lipid Membrane Simulations with AMBER.Lipid21:用 AMBER 进行复杂脂质膜模拟。
J Chem Theory Comput. 2022 Mar 8;18(3):1726-1736. doi: 10.1021/acs.jctc.1c01217. Epub 2022 Feb 3.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验