Reprogramming proteases: tackling human diseases with next-generation modulators

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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.