# Predictive modeling of viral RNA cellular behavior

> **NIH NIH DP5** · SCRIPPS RESEARCH INSTITUTE, THE · 2024 · $460,000

## Abstract

PROJECT SUMMARY/ABSTRACT
Modern biomedical science enjoys an unprecedented ability to identify and describe viral pathogenic
mechanisms, as well as characterize the biomolecular components that constitute them. However, we have not
yet achieved a fully quantitative biophysical understanding in which modeling of component molecules is
accurately predictive of viral functions in cells. Characterization of biomolecular structural dynamics, as opposed
to their static structures alone, is creating to new inroads to quantitative modeling of cellular function as well as
novel drug-targeting strategies. My recent work examining an RNA-protein interaction critical to HIV genome
transcription suggests that highly quantitative measurements and systematic mutant design, which specifically
perturbs dynamic properties, is a viable strategy for predicting viral RNA function in cells. In this proposal I plan
to use a multi-modal integrative approach to quantitatively measuring RNA dynamics for the purposes of building
a predictive model of RNA function and developing a novel RNA-targeting strategy. I will use the interaction of
the HIV 5’-leader RNA with the Gag polyprotein as the model system for these studies as it is a well-studied yet
complex interaction that involves critical interaction with the lipid bilayer and is also essential for the process of
viral genome packaging, making it a relevant drug target. In Aim 1 I will develop integrative high-throughput
(HTP) technologies that combine three-dimensional structural ensembles of component biomolecules with
quantitative measurements of in vitro and cellular functions to build predictive models of viral activity that can be
applied broadly. Specifically, I will construct a library of thousands of 5’-leader RNA mutants designed to
systematically perturb its structural dynamics. I will create a plasmid library of these sequences to develop an
RNA-Seq-based methodology to quantitatively measure the cellular activity of RNA mutants in HTP. I will then
use existing HTP methods such as RNA-MaP to evaluate the in vitro binding affinity of the same library of
sequences. Lastly, I will interpret this experimental data using structural dynamic ensembles of each RNA mutant,
determined from nuclear magnetic resonance (NMR)-informed structure prediction programs, to build a
predictive model of the 5’-leader:Gag interaction. By evaluating RNA mutants in HTP, I simultaneously screen
for non-functional, low abundance RNA conformations in cells that could represent both attractive drug targets
and tools for applications in synthetic biology. In Aim 2 I will identify and obtain ensemble descriptions of these
non-functional conformations using NMR and computational modeling and target them for antiviral drug
development using ensemble-based virtual screening (EBVS). I will also develop a fluorescence-based in vitro
screening assay involving the lipid bilayer with which to test hits from virtual screening as well as in-house s...

## Key facts

- **NIH application ID:** 10923737
- **Project number:** 1DP5OD037420-01
- **Recipient organization:** SCRIPPS RESEARCH INSTITUTE, THE
- **Principal Investigator:** Megan L Ken
- **Activity code:** DP5 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $460,000
- **Award type:** 1
- **Project period:** 2024-09-18 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10923737

## Citation

> US National Institutes of Health, RePORTER application 10923737, Predictive modeling of viral RNA cellular behavior (1DP5OD037420-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10923737. Licensed CC0.

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