# AWD13299 Admin Supplement to Support Undergraduate Summer Research Experiences

> **NIH NIH R35** · UNIVERSITY OF FLORIDA · 2023 · $10,138

## Abstract

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.

## Key facts

- **NIH application ID:** 10808664
- **Project number:** 3R35GM146821-01S1
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Carl Denard
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $10,138
- **Award type:** 3
- **Project period:** 2022-09-24 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10808664

## Citation

> US National Institutes of Health, RePORTER application 10808664, AWD13299 Admin Supplement to Support Undergraduate Summer Research Experiences (3R35GM146821-01S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10808664. Licensed CC0.

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