# New Strategies for Peptide Mimicry

> **NIH NIH R35** · NORTH CAROLINA STATE UNIVERSITY RALEIGH · 2022 · $364,450

## Abstract

ABSTRACT
Our research program is focused on the development of new broadly applicable tools for peptide mimicry.
Peptides are an important class of compounds that modulate a variety of biological responses, including
as hormones, neurotransmitters, and antimicrobials. Occupying the space in between conventional small
molecules and biologics, they can also be designed to inhibit large protein-protein interaction surfaces.
However, strategies are needed to convert them into more stable molecules while retaining the bioactivity
of the native peptides, ideally employing scaffolds that can adopt precise secondary structures while
being amenable to rapid derivatization for library synthesis and screening. Towards that goal, we develop
new methods for late-stage peptide functionalizations, and devise new strategies for the synthesis and
conformational control of acyclic, highly functionalized peptidomimetics. This includes (1) the study of
azapeptide folding and the development of new strategies for their chemoselective functionalization and
rigidification, and (2) the discovery and application of new conformationally-constrained N-substituted
glycine peptoid monomers. These novel methods are then applied to the synthesis of bioactive peptide
analogs.

## Key facts

- **NIH application ID:** 10500390
- **Project number:** 1R35GM147578-01
- **Recipient organization:** NORTH CAROLINA STATE UNIVERSITY RALEIGH
- **Principal Investigator:** Caroline Proulx
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $364,450
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10500390

## Citation

> US National Institutes of Health, RePORTER application 10500390, New Strategies for Peptide Mimicry (1R35GM147578-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10500390. Licensed CC0.

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