# Deciphering the principles of selective recognition of complex RNAs by small molecules

> **NIH NIH F30** · DUKE UNIVERSITY · 2021 · $51,036

## Abstract

ABSTRACT
Noncoding RNAs (ncRNAs) are a new and growing class of biomolecules that are increasingly being shown to
be important drug targets in many human pathogens. New drug targets are needed for many of the most
important pathogens in human disease as resistance, latency, and cost are still significant challenges in
treatment. While considerable effort has been made to target these essential ncRNAs, the most prominent
examples being stem loop hairpins in HIV, no compounds have successfully reached the market. These studies
have focused on simple RNAs that may not be ideal drug targets. Simple RNAs tend to have “low-druggability”
properties such high solvent exposure, limited unique binding sites, and increased flexibility. We hypothesize
that the lack of success in creating RNA-targeted small molecule drugs is due in large part due to difficulties in
targeting these hairpins with high selectivity, especially given that similar hairpin motifs occur in great abundance
in the transcriptome. However, there are a growing number of microbial ncRNA drug targets that fold into more
complex 3D structures. These RNAs are marked by unique secondary structural motifs, long-range tertiary
contacts, and often deeper and larger hydrophobic pockets. The main hypothesis of this proposal is that complex
RNA structures have attributes that make them better drug targets for small molecule inhibition as compared to
simpler hairpins. Aim 1 will used a structure-based survey, NMR-based binding assays, and computational
docking to test the hypothesis that complex RNAs have unique binding pockets and modes that allow highly
selective binding to ligands that otherwise only weakly bind to common hairpin RNAs. The Aim will also test the
hypothesis that the binding pockets of complex RNAs have variable attributes that result in varying levels of
binding selectivity, which can be predicted using computational docking. Aim 2 will use an ensemble-based
virtual screening approach, in combination with experimental high throughput screening, to test the hypothesis
that novel chemotypes bind to the pockets of complex RNAs, some of which can alter or inhibit biological function.
This project will provide a deeper understanding regarding the druggability of RNA, identify novel chemotypes
that may be further optimized for novel antimicrobial drugs, and help address the challenge of selectivity, which
is one of the biggest obstacles in targeting RNA with small molecules.
As very little drug discovery work has been done with these targets, this screening effort will vastly expand the
chemical space surveyed for RNA binding. Novel chemotypes that are discovered will lead to a deeper
understanding of the structural determinants of highly selective interactions and will become the basis of lead
compounds that can be further optimized for novel antimicrobial drugs.

## Key facts

- **NIH application ID:** 10231110
- **Project number:** 5F30AI143282-03
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Megan L Ken
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $51,036
- **Award type:** 5
- **Project period:** 2019-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10231110

## Citation

> US National Institutes of Health, RePORTER application 10231110, Deciphering the principles of selective recognition of complex RNAs by small molecules (5F30AI143282-03). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10231110. Licensed CC0.

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