Regulation of Gamma-Secretase by Substrate Inhibitory Domains

描述（由申请人提供）：阿尔茨海默病（AD）是一种破坏性的神经退行性疾病，影响540万美国人，但仍然没有FDA批准的治疗方法来治疗根本原因。AD的一个特征是淀粉样斑块的沉积，其由β-淀粉样（Aβ）肽聚集形成。根据“淀粉样蛋白级联假说”，Aβ肽在大脑中的积累触发了病理级联反应，导致神经变性并最终导致AD。γ-分泌酶是一种膜内蛋白酶复合物，控制许多蛋白质底物的加工。它是AD的一个有吸引力的药物靶标，因为它负责淀粉样前体蛋白（APP）蛋白水解的最后一步，以产生Aβ肽。早老素（γ-分泌酶催化亚基）和APP突变的遗传学证据支持开发γ-分泌酶抑制剂（GSI）治疗AD;然而，所有GSI的AD临床试验均失败。除了缺乏控制酶活性和特异性的分子基础知识之外，抑制非APP底物（如Notch）的副作用使其成为药物发现的挑战性靶点。为了开发具有改进的效力和选择性的GSI，需要更好地理解γ-分泌酶与其底物之间的相互作用。我们的实验室已经发现APP内的底物抑制结构域（ASID）通过结合酶复合物中的变构位点来负调节γ-分泌酶活性。有趣的是，与Notch裂解相比，ASID衍生的抑制剂优先减少γ-分泌酶介导的Aβ产生。底物内的这种抑制结构域可能代表γ-分泌酶和其他酶中的共同调节机制。本申请的目的是确定γ-分泌酶底物的抑制结构域如何发挥调节酶的功能。我们假设γ-分泌酶以组成型活性形式存在，并且底物本身作为关键调节剂通过独特的抑制结构域调节γ-分泌酶对其自身的切割。破坏APP中的这种负调节可能导致Aβ产生升高，并最终导致AD。通过详细的生物化学表征，本研究旨在：1）确定ASID如何与APP的其他区域相互作用以调节γ-分泌酶; 2）鉴定Notch 1底物中的底物抑制结构域（ASID）并表征其与γ-分泌酶的相互作用。为了研究ASID的作用机制，我们将合成多肽和化学探针来检测ASID位点与γ-分泌酶的对接位点和活性位点的关系。我们将开发一系列的Notch 1底物和抑制剂来验证Notch底物抑制结构域的存在并阐明其调控机制。拟开展的研究将通过位于底物内的抑制结构域来解码这种新型γ-分泌酶调节的分子机制。这项提案的结果将为反倾销的基本过程提供重要的见解，并为开发更有效的全球统计信息开辟新的途径。

DESCRIPTION (provided by applicant): Alzheimer's disease (AD) is a devastating neurodegenerative disorder that affects 5.4 million Americans, yet there are still no FDA-approved therapies to treat the underlying causes. One AD characteristic is the deposition of amyloid plaques, which are formed by aggregation of β-amyloid (Aβ) peptides. According to the "amyloid cascade hypothesis", the accumulation of Aβ peptides in the brain triggers a pathological cascade that causes neurodegeneration and eventually leads to AD. γ-Secretase is an intramembrane protease complex that controls the processing of numerous protein substrates. It is an appealing drug target for AD since it is responsible for the final step of amyloid precursor protein (APP) proteolysis to generate Aβ peptides. Genetic evidence from mutations in presenilin (γ-secretase catalytic subunit) and APP supports the development of γ-secretase inhibitors (GSIs) to treat AD; however, all AD clinical trials of GSIs have failed. Side effects from inhibition of non-APP substrates (such as Notch) in addition to a lack of knowledge of the molecular basis for controlling enzyme activity and specificity have rendered it a challenging target for drug discovery. In order to develop GSIs with improved potency and selectivity, a better understanding of the interactions between γ-secretase and its substrates is needed. Our lab has discovered a substrate inhibitory domain (ASID) within APP that negatively modulates γ-secretase activity by binding to an allosteric site in the enzyme complex. Interestingly, ASID-derived inhibitors preferentially reduce γ-secretase-mediated Aβ production over Notch cleavage. Such an inhibitory domain within the substrate could represent a common regulatory mechanism in γ-secretase and other enzymes. The objective of this application is to determine how the inhibitory domain of γ-secretase substrates functions to modulate the enzyme. We hypothesize that γ-secretase exists in a constitutively active form and that the substrates themselves serve as key regulators to modulate their own cleavage by γ-secretase via unique inhibitory domains. Disruption of this negative modulation in APP can potentially result in elevated Aβ production and ultimately lead to AD. Through detailed biochemical characterization, this study aims to: 1) determine how ASID interacts with other regions of APP to regulate γ-secretase and 2) identify a substrate inhibitory domain (ASID) in the Notch1 substrate and characterize its interaction with γ-secretase. In order to investigate the mechanism of action for ASID, we will synthesize peptides and chemical probes to examine the relationship of the ASID site with the docking site and active site of γ-secretase. We will develop a series of Notch1 substrates and inhibitors to validate the existence of a Notch substrate inhibitory domain and elucidate its regulatory mechanism. The proposed studies will decode the molecular mechanism of this novel regulation of γ-secretase through the inhibitory domains located within the substrates. Findings from this proposal will provide critical insight into the processes underlying AD and open a new avenue for development of more effective GSIs.