# Next-generation computational/chemical methods for complex RNA structures

> **NIH NIH R35** · STANFORD UNIVERSITY · 2020 · $680,099

## Abstract

PROJECT SUMMARY
The continuing discoveries of non-coding RNAs and their critical roles in cellular and
viral machinery are inspiring novel antibacterial, antitumor, antiviral, and genome-editing
therapies based on disabling, manipulating, and repurposing the RNAs involved.
Unfortunately, our poor biophysical understanding of `how RNAs work' hinders the
development of these potentially life-saving efforts. A critical bottleneck has been the
inapplicability of crystallography, NMR, cryoelectron microscopy, phylogenetic analysis,
and biochemical methods to determine the partly ordered conformations of non-coding
RNAs in all their functional states. To resolve this bottleneck, we are developing
experimental methods and complementary computational approaches that give rich
information sufficient to infer and engineer RNA secondary and tertiary structures and
their heterogeneous ensembles, evaluated through community-wide blind trials,
prospective compensatory mutation/rescue experiments, and global RNA design
challenges. Here, we outline expansions of our research that will rigorously address four
biomedically significant problems that have so far seen limited progress in molecular
modeling efforts: the heterogeneity of RNA structures within their native cellular and viral
contexts; modeling and design of RNA's biological interactions with proteins and other
molecules, modulated by chemical modification; high-accuracy calculation of RNA
folding energetics; and the automated design of dynamic 3D RNA structures for eventual
medical applications. We will evaluate success through continuing blind trials,
independent tests by more than a dozen expert biological and bioengineering
collaborators, and through adoption of our methods and software tools by the broader
research community. In the same way that specialized structural biology tools and
computational design are establishing a firm understanding of protein behavior and
regulation, we propose that the technologies outlined here will transform our
understanding of structure in non-coding RNAs, providing a stronger basis for their
biomedical activation or disruption.

## Key facts

- **NIH application ID:** 9990810
- **Project number:** 5R35GM122579-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Rhiju Das
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $680,099
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9990810

## Citation

> US National Institutes of Health, RePORTER application 9990810, Next-generation computational/chemical methods for complex RNA structures (5R35GM122579-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9990810. Licensed CC0.

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