Developing computational methods to identify of endogenous substrates of E3 ubiquitin ligases and molecular glue degraders

开发计算方法来鉴定 E3 泛素连接酶和分子胶降解剂的内源底物

• 批准号: 10678199
• 负责人: Victoria Mischley
• 金额: $ 4.94万
• 依托单位: DREXEL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10678199
• 关键词: ADAR1; Address; Autoimmune Diseases; Benchmarking; Binding; Biological; Biological Assay; Biology; Catalogs; Cells; Complex; Computing Methodologies; Data; Descriptor; Development; Disease; Equilibrium; Future; Glues; Goals; Human; Immunotherapy; Intervention; Laboratories; Ligase; Machine Learning; Malignant Neoplasms; Measures; Mediating; Methods; Modality; Modeling; Molecular; Mutation; Nature; Network-based; Polyubiquitin; Proteins; Proteome; Research Personnel; Resistance; Specificity; Structure; Substrate Interaction; Substrate Specificity; Testing; Time; Training; Ubiquitin; Ubiquitination; computerized tools; design; drug discovery; human disease; immune checkpoint blockade; improved; insight; interest; machine learning classifier; machine learning method; model building; multicatalytic endopeptidase complex; neural network; novel; pharmacologic; prevent; protein degradation; protein protein interaction; protein structure prediction; rational design; response; small molecule; small molecule libraries; structural biology; ubiquitin-protein ligase; yeast two hybrid system

Project Summary/ Abstract: The E1-E2-E3 ligase cascade is responsible for tagging substrate proteins with ubiquitin. Addition of ubiquitin then directs the tagged protein along one of several paths, including marking it for proteasome-mediated degradation. The E3 ligase is responsible for recognition of substrate proteins, and thus encodes the specificity of ubiquitin transfer. The human proteome comprises about 600 known E3 ligases, each with a distinct substrate specificity that allows it to engage a prescribed subset of the proteome. Given that one of the prototypical consequences of ubiquitination is to mark a protein for destruction, it is unsurprising that dysregulation or mutation of E3 ligases can lead to a disruption of cellular homeostatic balance: accordingly, E3 ligases have been implicated in a wide variety of diseases including autoimmune disease and cancer. Intriguingly, certain disease-associated mutations have been found to alter the substrate specificity of an E3 ligase – these mutations not only impact the cellular levels of proteins within the E3’s normal interactome, but rather they change the E3 ligase’s interactome. Similar effects have also been observed from certain small molecules, termed molecular glues, that also modify the substrate specificity of an E3 ligase: these compounds typically redirect an E3 ligase to ubiquitinate some “neo-substrate”, ultimately leading to degradation of this protein. Thus, molecular glues afford the possibility of targeting disease-causing proteins that were previously thought to be undruggable. To date, however, the transient nature of E3 ligase’s interactions with their substrates (and neo-substrates) has served as a bottleneck for identifying both endogenous and “glue-able” substrates of E3 ligases. To address this, here I propose to develop cutting-edge structure-based machine learning methods to (1) computationally identify endogenous substrates of E3 ligases, and (2) rationally design molecular glues that degrade a traditionally undruggable target protein. After carefully benchmarking the underlying methods for each task, I will apply the former to comprehensively catalog substrates of three specific disease-relevant E3 ligases. In parallel, I will apply my approach for the latter to design molecular glues intended to degrade ADAR1, a key protein that promotes resistance to immune checkpoint blockade therapy and is thus a potential target for intervention in many different cancers. Beyond the immediate scope of this proposal, I anticipate that the methods developed through these studies will help illuminate the underlying biology of many other E3 ligases, and will facilitate development of molecular glue degraders targeting key drivers in many other diseases.

项目概要/摘要： E1-E2-E3连接酶级联负责用泛素标记底物蛋白。添加泛素 然后引导标记蛋白沿着几条路径之一，包括标记蛋白酶体介导的 降解E3连接酶负责识别底物蛋白，因此编码特异性 泛素转移的机制人类蛋白质组包含约600种已知的E3连接酶，每种具有不同的结构域。 底物特异性，使其能够参与蛋白质组的规定子集。考虑到其中一个 泛素化的典型后果是标记蛋白质以进行破坏，这并不奇怪， E3连接酶的失调或突变可导致细胞稳态平衡的破坏：因此， E3连接酶与包括自身免疫性疾病和癌症在内的多种疾病有关。 有趣的是，已经发现某些疾病相关的突变改变了E3的底物特异性。 连接酶-这些突变不仅影响E3正常相互作用组内蛋白质的细胞水平， 相反，它们改变了E3连接酶的相互作用体。类似的效果也从一些小的 分子，称为分子胶，也改变E3连接酶的底物特异性：这些化合物 通常将E3连接酶重定向到泛素化一些“新底物”，最终导致这种“新底物”的降解。 蛋白因此，分子胶提供了靶向致病蛋白质的可能性，这些蛋白质以前是 被认为是不可治愈的然而，到目前为止，E3连接酶与它们的相互作用的瞬时性质还没有得到证实。 底物（和新底物）已经成为鉴别内源性和“可胶”底物的瓶颈。 E3连接酶的底物。为了解决这个问题，我在这里建议开发尖端的基于结构的机器 学习方法（1）计算鉴定E3连接酶的内源性底物，和（2）合理设计 降解传统上不可用的靶蛋白的分子胶。经过仔细的基准测试， 每个任务的基本方法，我将应用前者全面目录基板的三个 特异性疾病相关的E3连接酶。同时，我将应用我的方法为后者设计分子 旨在降解ADAR 1的胶水，ADAR 1是一种促进对免疫检查点阻断的抵抗的关键蛋白质 因此，它是许多不同癌症中干预的潜在靶标。超出的范围 根据这项建议，我预计通过这些研究开发的方法将有助于阐明 生物学的许多其他E3连接酶，并将促进分子胶降解剂的发展，针对关键 司机在许多其他疾病。