Reprogramming proteases: tackling human diseases with next-generation modulators

项目总结 尽管众所周知，蛋白水解酶参与了疾病的病理生理过程，但由此产生的 发现蛋白水解酶药物的挑战是设计或分离选择性地抑制或激活的特定配体。 一种用于治疗疾病状态和促进机械性研究的靶标蛋白酶。然而，除了好的- 研究血管紧张素转换酶和艾滋病毒蛋白水解酶等酶，开发新药 事实证明，蛋白酶靶标是一个反复的、艰巨的、往往不成功的过程。认识到 配体的性质最终决定了它的调节功能和结合机制，拟议的研究 假设了两个假设。首先，如果分子是根据它们的调制功能直接从 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，配体性质和调节剂的功能关系 机制是可以开发的，并可能将这些发现扩展到相关的蛋白酶。建议数 研究追求三个方向，总的目标是转变蛋白酶配体的发现和 从迭代努力到数据驱动和最终可预测的过程的蛋白酶生物化学。第一 研究方向将建立机器学习(ML)引导的高通量筛选平台 根据蛋白水解酶调节剂如何改变蛋白酶功能直接分离它们。在这里，财产- 函数关系将训练用于函数预测和ML指导库设计的机器学习算法 在探索末端调控多样性的同时，将显著减少对蛋白酶调节剂的搜索空间 更全面。在第二个研究方向，该平台将被扩展到分离纳米体- 基于底物选择性调节剂的β分泌酶和胰岛素降解酶，这两种酶是关键 阿尔茨海默病和2型糖尿病的治疗靶点。精细重新编程的能力 蛋白酶的底物选择性可以革命性地研究和治疗多特异酶，并导致 成功地瞄准了以前无法下药的蛋白酶。第三个研究方向将深入实施 突变扫描协议，绘制蛋白水解酶的调制图谱，并确定调节子如何 在分子和生理水平上改变蛋白酶底物的偏好。此方法将标识 调节子的构象表位，表征新的远端位点，并发现长距离远端 沟通。总而言之，这些研究的长期回报是建立可推广的配体 基于配基性质、结合机制/蛋白酶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是如何工作的，以及如何控制 他们。丹纳德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，有力地支持了可行性， 这一多学科的、可能产生广泛影响的独立项目的成功和可持续性。

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.