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

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.