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.

项目摘要/摘要： E1-E2-E3 连接酶级联负责用泛素添加标记底物蛋白。 然后引导标记的蛋白质沿着几种路径之一，包括将其标记为蛋白酶体介导的 E3 连接酶负责识别底物蛋白，从而编码特异性。 人类蛋白质组包含约 600 种已知的 E3 连接酶，每种都具有独特的特征。 底物特异性使其能够结合蛋白质组的指定子集。 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能导致细胞稳态平衡的破坏：因此， E3 连接酶与多种疾病有关，包括自身免疫性疾病和癌症。 有趣的是，某些与疾病相关的突变被发现会改变 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，而且 相反，它们改变了 E3 连接酶的相互作用组，在某些小分子中也观察到了类似的效果。 分子，称为分子胶，也可以改变 E3 连接酶的底物特异性：这些化合物 通常会重定向 E3 连接酶以泛素化某些“新底物”，最终导致该底物降解 因此，分子胶提供了针对以前的致病蛋白质的可能性。 然而，迄今为止，E3 连接酶与它们的相互作用是短暂的。 基材（和新基材）已成为识别内源性和“可粘合”的瓶颈 为了解决这个问题，我在这里建议开发基于尖端结构的机器。 学习方法 (1) 通过计算识别 E3 连接酶的内源底物，以及 (2) 合理设计 在仔细对标后，分子胶可降解传统上不可成药的目标蛋白质。 对于每项任务的基本方法，我将应用前者来全面编目三个任务的底物 同时，我将应用我的方法来设计后者的分子。 旨在降解 ADAR1 的胶水，ADAR1 是一种促进免疫检查点封锁抵抗力的关键蛋白质 治疗，因此是干预许多不同癌症的潜在目标。 对于这项建议，我预计通过这些研究开发的方法将有助于阐明潜在的 许多其他 E3 连接酶的生物学特性，并将促进针对关键分子胶降解剂的开发 许多其他疾病的驱动因素。