AWD13299 Admin Supplement to Support Undergraduate Summer Research Experiences

1 R35GM146821-01的项目摘要 尽管蛋白水解酶在疾病病理生理学中起着主要作用，但在 蛋白水解酶药物的发现是设计或分离一种特定的配体，它选择性地抑制或 激活目标蛋白水解酶。改进现有的蛋白水解酶药物，开发新的药物 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 配体的性质最终决定了它的调节功能和结合机制， 这项拟议的研究假设了两个假设。首先，如果分子被直接选择 基于它们来自大型文库的调节功能，它们的性质将直接与它们的 函数，而不是它们的绑定能力。其次，如果结合机制是调节器 确定的，配基性质和机理之间的功能关系可以是 开发了这些发现，并可能将其扩展到相关的蛋白酶。拟议的研究 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并最终可预测的蛋白酶生物化学 流程。第一个研究方向是建立一个以机器学习(ML)为指导的高性能 一种直接分离基于蛋白水解酶调节剂的吞吐量筛选平台 它们是如何改变蛋白酶功能的。在这里，属性-函数关系将训练机器 用于函数预测和ML指导库设计的学习算法将显著减少 在探索远端调控多样性的同时寻找蛋白酶调节子的搜索空间 全面的。在第二个研究方向上，该平台将扩展到隔离 基于纳米体基的β分泌酶和胰岛素降解酶的底物选择性调节剂 两种蛋白酶是阿尔茨海默病和2型糖尿病的关键治疗靶点， 分别进行了分析。精细地重新编程蛋白酶的底物选择性的能力可以 革命性地研究和用药多特异酶，并成功靶向 以前不能下药的蛋白酶。第三个研究方向将实现深度突变 扫描协议以绘制蛋白水解酶的调制图景并确定如何 调节剂改变了酶的活性和底物的选择性。此方法将标识 调节剂的构象表位，绘制耐药图，表征新的远端位点，以及 发现远距离远端通讯。总而言之，这些研究的长期回报 是建立基于三元关系的可概括的配体设计指南 配基性质、结合机制/蛋白酶结构和调节功能，使人们能够 更好地了解蛋白酶是如何工作的，以及如何控制它们。

Project Summary for 1 R35GM146821-01 Although proteases play major roles 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. Improving current protease drugs and 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 activity and substrate selectivity. This approach will identify conformational epitopes of modulators, map drug resistance, 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.