# ConProject-001

> **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2020 · $336,997

## Abstract

The most well-studied RNA molecular structures, notably tRNA and rRNA, exist as a single dominant conformation.
However, a growing number of small non-coding RNA sequences are known to function by switching between
multiple stable configurations. It is expected that such multi modal structural motifs punctuate the ensemble of
low-energy structures for an RNA viral genome like Chikungunya, regulating the viral lifecycle. Characterizing
these small overlapping sets of stable base pairs, embedded in lengthy sequences with high structural diversity, is
essential to understanding how critical structural signals encode the functionality of these important pathogens.
This collaboration leverages complementary strengths of previous results --- mining competing signals from the
structural ensemble (profiling) and next generation chemical footprinting (SHAPE-MaP) --- to tackle the challenge
of multi modal motif discovery in a test set of three alphavirus genomes. This first aim will be achieved by
developing the necessary characterizations of profiling landscapes and of SHAPE-MaP signatures to identify target
regions with multiple native conformations. These separate results will be validated in individual sequences by the
combination of SHAPE-directed profiling, following experimental confirmation of the current prediction
methodology. The second aim will demonstrate evolutionary support for these new motifs, first across the three
test sequences and then the entire alphaviral family, through a new application of computational algebraic
topology. Persistent homology and simplicial complexes will be used to analyze evolution across the different
scales at which biological information is encoded in RNA viral genomes, ranging from genomic sequence to
vertebrate host. This will be followed by chemical probing confirmation for three additional alphavirus sequences.
This project will extend the frontiers of RNA folding by integrating new mathematical models and analyses
based on combinatorics and algebraic topology with recent advances in the biochemistry of chemical footprinting
for the purposes of identifying significant motifs with multimodal structure in lengthy RNA viral genomes. The
results of this study, a set of novel secondary structure motifs in alphavirus genomes which are ideal candidates for
further investigation as important functional elements, will be a key resource for RNA virologists. Furthermore, the
proposed theoretical and algorithmic developments are generally applicable to all RNA viruses, and hence of
significant utility and interest to the scientific community.

## Key facts

- **NIH application ID:** 9986785
- **Project number:** 5R01GM126554-04
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Christine E Heitsch
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $336,997
- **Award type:** 5
- **Project period:** 2017-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9986785

## Citation

> US National Institutes of Health, RePORTER application 9986785, ConProject-001 (5R01GM126554-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9986785. Licensed CC0.

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