CAREER: Selectively Reprogramming Proteases through the High-Throughput Discovery of Functional Protein-Protein Interactions.

蛋白水解酶是一种裂解蛋白质中的多肽键的酶。它们大量存在于所有生物体中，并在调节细胞过程中发挥关键作用。如果他们的活动受到损害，就可能出现疾病。这些疾病的范围从传染病到癌症和神经变性。这个项目的主要研究目标是发现和重新设计可以重新编程蛋白酶的蛋白质分子。主要的教育目标是开发针对K-5学生的合成生物学STEM漫画系列。由于蛋白水解酶在细菌和病毒的致病性中起着核心作用，因此，蛋白水解酶的失调是一种疾病指标。提出了一个实验和计算框架，以了解蛋白酶是如何工作的，以及如何重新编程它们。这将需要开发以功能为中心的技术来发现基于蛋白质的结合，这些结合可以重新编程蛋白酶的催化活性和底物专一性。目的1是从合成纳米体库中分离出选择性的、有效的抑制性和刺激性纳米体来对抗四组蛋白水解酶，包括人的蛋白水解酶靶标。目标2是利用深度学习通过使用NB序列函数数据设计机器学习优化的NB库来加速调制器的发现。目的3通过NB介导的蛋白质-蛋白质相互作用(PPI)和蛋白酶工程，绘制胰岛素降解酶(IDE)的功能图谱。深度测序数据分析将阐明新型功能性PPI的序列-功能图景。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Proteases are enzymes that cleave peptide bonds in proteins. They abound in all organisms and play key roles in regulating cellular processes. If their activity is impaired, diseases can arise. These range from infectious diseases to cancer and neurodegeneration. The main research objective of this project is to discover and redesign proteinaceous molecules that can reprogram proteases. The major educational objective is to develop a STEM comic series on synthetic biology targeted at K-5 students. Protease dysregulation is a disease indicator, as proteases play central roles in bacterial and viral pathogenicity. An experimental and computational framework is proposed to understand how proteases work and how to reprogram them. This will require the development of function-centric technologies to discover protein-based binders that can reprogram a protease’s catalytic activity and substrate specificity. Aim 1 is to isolate selective, potent inhibitory and stimulatory nanobodies from a synthetic nanobody (Nb) library against proteases from four groups, including human protease targets. Aim 2 is to leverage deep learning to accelerate modulator discovery by designing machine learning-optimized Nb libraries using Nb sequence-function data. Aim 3 is to map the functional landscape of insulin-degrading enzyme (IDE) through Nb-mediated protein-protein interactions (PPIs) and protease engineering. Deep sequencing data analysis will elucidate the sequence-function landscape for novel functional PPIs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.