Substrate recruitment and cleavage by γ-secretase

γ-分泌酶的底物招募和裂解

• 批准号: 280705768
• 负责人: Professor Dr. Harald Steiner
• 金额: --
• 依托单位: Lehrstuhl für Stoffwechselbiochemie
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/280705768?language=en
• 关键词: Substrate; recruitment; cleavage; secretase

gamma-Secretase is a pivotal intramembrane protease and major Alzheimer disease (AD) drug target. Besides the AD-associated beta-amyloid precursor protein (APP), currently in the range of hundred substrates are known to be cleaved by gamma-secretase. How these are recognized and selected is only poorly understood. Despite recent structural information of gamma-secretase, the substrate-binding site(s) of the enzyme are not yet defined. Moreover, it is not known which substrate features make a gamma-secretase substrate a good (i.e. efficiently cleavable) or a bad (i.e. poorly cleavable) one, respectively. In this project proposal, we will identify the substrate-binding sites of gamma-secretase using engineered substrates carrying photocrosslinkable amino acids at defined positions. Thus, upon UV irradiation, the subunits, which directly bind substrate will be unambiguously identified. Using this approach, which will be introduced to our research field for the first time, we will initially start to map the binding sites of APP and then extend this analysis to the crucial gamma-secretase substrate Notch1 and other substrates of interest (goal 1). By mutational analysis, we will additionally identify and define sequence determinants that govern the efficiency of substrate cleavage in proteolysis assays (goal 2). Finally, we will analyze how the lipid environment of the protease will modulate the substrate recognition process (goal 3). Our studies will provide fundamental insights into how intramembrane proteases recruit and cleave its substrates.

γ-分泌酶是一种重要的膜内蛋白酶，也是阿尔茨海默病（Alzheimer disease，AD）的主要药物靶点。除了AD相关的β-淀粉样前体蛋白（APP），目前已知有数百种底物被γ-分泌酶切割。如何识别和选择这些只是知之甚少。尽管最近的结构信息的γ-分泌酶，底物结合位点的酶尚未确定。此外，还不知道哪些底物特征分别使γ-分泌酶底物成为好的（即可有效裂解的）或坏的（即可较差裂解的）底物。在这个项目建议中，我们将确定γ-分泌酶的底物结合位点使用工程基板携带光交联氨基酸在定义的位置。因此，在UV照射时，直接结合底物的亚基将被明确地鉴定。使用这种方法，这将是第一次引入我们的研究领域，我们将首先开始绘制APP的结合位点，然后将这种分析扩展到关键的γ-分泌酶底物Notch 1和其他感兴趣的底物（目标1）。通过突变分析，我们还将鉴定和定义在蛋白水解测定中控制底物切割效率的序列决定因素（目标2）。最后，我们将分析蛋白酶的脂质环境将如何调节底物识别过程（目标3）。我们的研究将为膜内蛋白酶如何招募和切割其底物提供基本的见解。