Reprogramming proteases: tackling human diseases with next-generation modulators

项目摘要 尽管蛋白酶被广泛地认为参与疾病病理生理学，但随之而来的是， 蛋白酶药物发现中的挑战是设计或分离选择性抑制或激活 目标蛋白酶，以补救疾病状态和促进机制研究。但是，除了好- 研究了血管紧张素转换酶和HIV蛋白酶等酶，开发了新的药物， 蛋白酶靶向已被证明是一个反复的、费力的且经常不成功的过程。认识到 配体的性质最终决定了其调节功能和结合机制， 提出了两个假设。首先，如果直接根据分子的调节功能选择分子， 对于大型库，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。第二、 如果确定了调节剂的结合机制，则可以确定配体性质和 机制可以开发，并可能将这些发现扩展到相关的蛋白酶。拟议 研究追求三个方向，总体目标是将蛋白酶配体的发现和 蛋白酶生物化学从迭代的努力，以数据驱动，并最终预测过程。第一 研究方向将建立一个机器学习（ML）引导的高通量筛选平台， 直接根据蛋白质如何改变蛋白酶功能来分离蛋白质基蛋白酶调节剂。在这里，财产- 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 在探索远端调节多样性的同时， 更全面地。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 阿尔茨海默病和2型糖尿病的治疗目标。精细地重新编程的能力 蛋白酶底物选择性可以彻底改变如何研究和药物多特异性酶， 成功地靶向了以前不可用的蛋白酶。第三个研究方向将深入实施 突变扫描协议，以映射蛋白酶的调节景观，并确定调节剂如何 在分子和生理尺度上改变蛋白酶底物偏好。该方法将识别 调节剂的构象表位，表征新的远端位点，并揭示长距离远端位点。 通信总之，这些研究的长期回报是建立可推广的配体 基于配体性质、结合机制/蛋白酶之间的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 他们Denard研究实验室在高通量蛋白酶工程和支持方面的丰富经验 分别来自机器学习专家和神经科学专家，强烈支持这种可行性， 成功，和这个多学科的可持续性，并可能广泛影响的独立计划。

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.