Reprogramming proteases: tackling human diseases with next-generation modulators

重编程蛋白酶：用下一代调节剂应对人类疾病

• 批准号: 10709575
• 负责人: Carl Denard
• 金额: $ 27.19万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-24 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10709575
• 关键词: Acceleration; Alzheimer' s Disease; Autoimmune Diseases; Binding; Biochemistry; Communicable Diseases; Communication; Data; Disease; Distal; Engineering; Enzymes; Epitopes; Functional disorder; Goals; Guidelines; HIV Protease; Insulinase; Investigation; Libraries; Ligands; Machine Learning; Malignant Neoplasms; Maps; Molecular; Molecular Conformation; Nerve Degeneration; Neurosciences; Non-Insulin-Dependent Diabetes Mellitus; Peptide Hydrolases; Peptidyl-Dipeptidase A; Pharmaceutical Preparations; Physiological; Post-Translational Protein Processing; Process; Property; Proteins; Protocols documentation; Regulation; Research; Site; Structure; Training; Work; beta secretase; design; drug discovery; experience; high throughput screening; human disease; machine learning algorithm; multidisciplinary; mutation screening; nanobodies; next generation; novel; preference; programs; remediation; success; therapeutic target; tool

PROJECT SUMMARY Although proteases are widely known to be involved in disease pathophysiology, a consequential challenge in protease drug discovery is to design or isolate a specific ligand that selectively inhibits or activates a target protease to remediate disease states and facilitate mechanistic investigations. However, besides well- studied enzymes such as angiotensin-converting enzyme and HIV protease, developing drugs for new protease targets has proven an iterative, arduous, and often unsuccessful process. Recognizing that the property of a ligand ultimately dictates its modulatory function and binding mechanism, the proposed research postulates two hypotheses. First, if molecules are selected directly based on their modulatory function from large libraries, their properties will directly relate to their function, rather than their binding capabilities. Second, if the binding mechanism a modulator is determined, functional relationships between ligand properties and mechanism can be developed and possibly extended these findings to related proteases. The proposed research pursues three directions, with an overall objective to transform protease ligand discovery and protease biochemistry from iterative endeavors to data-driven, and ultimately predictive processes. The first research direction will establish a machine learning (ML)-guided high-throughput screening platform that isolates protein-based protease modulators directly based on how they alter protease function. Here, property- function relationships will train machine learning algorithms for function prediction and ML-guided library design will significantly reduce the search space for protease modulators while exploring distal regulation diversity more comprehensively. In a second research direction, this platform will be extended to isolate nanobody- based substrate selective modulators of β-secretase and insulin-degrading enzyme, two proteases that are key therapeutic targets in Alzheimer’s disease and Type-2-Diabetes, respectively. The ability to finely reprogram the substrate selectivity of proteases can revolutionize how to study and drug polyspecific enzymes and lead to successfully targeting previously undruggable proteases. The third research direction will implement deep mutational scanning protocols to map the modulatory landscape of proteases and determine how modulators alter protease substrate preference at the molecular and physiological scale. This approach will identify conformational epitopes of modulators, characterize novel distal sites, and uncover long-range distal communication. Taken together, the long-term payoff of these studies is to establish generalizable ligand design guidelines based on ternary relationships between ligand property, binding mechanism/protease structure and modulatory function, enabling one to better understand how proteases work and how to control them. The vast experience of the Denard research lab in high-throughput protease engineering and support from a machine learning expert and neuroscience expert, respectively, strongly supports the feasibility, success, and sustainability of this multidisciplinary, and potentially broadly impactful independent program.

项目概要 尽管众所周知蛋白酶与疾病病理生理学有关，但随之而来的 蛋白酶药物发现的挑战是设计或分离选择性抑制或激活的特定配体 一种修复疾病状态并促进机制研究的目标蛋白酶。 研究血管紧张素转换酶和艾滋病毒蛋白酶等酶，开发新的药物 蛋白酶靶标已被证明是一个反复、艰巨且常常不成功的过程。 配体的性质最终决定了其调节功能和结合机制，拟议的研究 首先，假设分子是直接根据其调节功能来选择的。 大型库，它们的属性将直接与它们的功能相关，而不是它们的绑定能力。 如果调节剂的结合机制已确定，配体特性和配体之间的功能关系 可以开发机制并可能将这些发现扩展到相关的蛋白酶。 研究追求三个方向，总体目标是改变蛋白酶配体的发现和 蛋白酶生物化学从迭代努力到数据驱动，以及最终的预测过程。 研究方向将建立一个机器学习（ML）引导的高通量筛选平台， 直接根据它们如何改变蛋白酶功能来分离基于蛋白质的蛋白酶调节剂。 函数关系将训练用于函数预测和 ML 引导的库设计的机器学习算法 将显着减少蛋白酶调节剂的搜索空间，同时探索远端调节多样性 在第二个研究方向中，该平台将扩展到分离纳米抗体。 基于β-分泌酶和胰岛素降解酶的底物选择性调节剂，这两种蛋白酶是关键 分别是阿尔茨海默病和 2 型糖尿病的治疗靶标。 蛋白酶的底物选择性可以彻底改变多特异性酶的研究和药物治疗方式，并导致 第三个研究方向将成功针对以前无法成药的蛋白酶。 突变扫描方案绘制蛋白酶的调节图谱并确定调节剂如何 这种方法将在分子和生理水平上改变蛋白酶底物偏好。 调节剂的构象表位，表征新的远端位点，并揭示长程远端 总而言之，这些研究的长期回报是建立可推广的配体。 基于配体特性、结合机制/蛋白酶之间三元关系的设计指南 结构和调节功能，使人们能够更好地了解蛋白酶如何工作以及如何控制 Denard 研究实验室在高通量蛋白酶工程和支持方面拥有丰富的经验。 分别来自机器学习专家和神经科学专家，强烈支持可行性， 这个多学科且具有潜在广泛影响力的独立项目的成功和可持续性。