Friday, April 26, 2024
4/26/2024
Project Summary Modulating the activity of proteases is a central strategy for treating cancer, autoimmunity, and infection. However, the discovery and design of selective and potent therapeutics targeting proteases (small-molecules and antibodies) largely rely on inefficient, iterative processes. As a result, it takes several years to develop a protease drug, and even then, most protease drugs are active site inhibitors that often suffer from low selectivity in distinguishing related proteases. Due to the complexity of proteolytic dysregulation, restoring homeostasis requires not only selective inhibitors but also ligands that can reprogram protease selectivity. Unfortunately, no platform exists to engineer protease modulators based on systematic, and quantitative design principles. To address these challenges, this proposal seeks to combine for the first time Machine Learning tools, Next- Generation DNA sequencing, and a yeast-based high-throughput functional screen to accelerate the isolation and design of nanobody-based protease modulators. The functional selection will perform two tasks: (i) select nanobodies from synthetic libraries based on a desired function and (ii) correlate ligand: epitope interactions to a functional outcome. These experiments will generate high-quality datasets that will train machine learning algorithms (ML) to predict the potency, selectivity, and mechanisms of nanobody-based modulators based on their sequence features alone. This machine learning-aided strategy will accelerate the discovery of rare and potent protease modulators and bypass the limitations of structure-based methods. Moreover, curated datasets of protease modulatory nanobody sequences will provide reference and design guidelines for future experimental and in silico campaigns. This work is of significant interest to biomedical research and public health and includes select proteases such as Hepatitis C virus protease, MMPs, transmembrane serine protease 2 (COVID-19), ß-secretase, and insulin-degrading enzyme. Moreover, the proposed studies provide a foundation to answering fundamental biochemical questions on how synthetic ligands can map and modulate the functional landscape of proteases and other protein-modifying enzymes.
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