# Automated Comparative Sequence Analysis of RNA Secondary and Tertiary Structure

> **NIH NIH R01** · UNIVERSITY OF ROCHESTER · 2020 · $308,000

## Abstract

Project Summary:
 RNA serves important roles as an information carrier and effector molecule. For many roles, a
functional RNA must adopt a specific secondary or tertiary structure. Across evolution, these structures are
more conserved than the sequence. By comparing multiple homologous sequences, structures can be inferred.
We developed TurboFold as an accurate and rapid method for automating sequence comparison to predict
conserved RNA secondary structures and alignments. We also expanded this with a knowledge-based
potential to predict conserved non-canonical base pairs, which are the basis of tertiary structures.
 Expanding on our TurboFold method, we will develop new high-impact algorithms and software to solve
important problems in RNA biology. First, we will develop new tools to estimate phylogenies for RNA and to
use the phylogenetic relationships between sequences to more accurately predict structures and alignments.
Second, we will improve tertiary structure modeling by using our predictions of conserved non-canonical pairs
as restraints for building all-atom models. Third, we will develop new tools for homology modeling of
secondary and tertiary structure, where a template structure for an RNA sequence from the same family exists.

## Key facts

- **NIH application ID:** 9903401
- **Project number:** 5R01GM132185-02
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** DAVID H. MATHEWS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $308,000
- **Award type:** 5
- **Project period:** 2019-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9903401

## Citation

> US National Institutes of Health, RePORTER application 9903401, Automated Comparative Sequence Analysis of RNA Secondary and Tertiary Structure (5R01GM132185-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9903401. Licensed CC0.

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