# High-resolution modeling of protein-RNA interfaces

> **NIH NIH R01** · FRED HUTCHINSON CANCER RESEARCH CENTER · 2020 · $300,244

## Abstract

SUMMARY
RNA-protein interactions mediate multiple critical regulatory steps in the translation of information from the
genome to the cellular machine, and corruptions of these fundamental interactions are implicated throughout
infectious and inherited disease, neurodegeneration, and cancer. Unfortunately, a scarcity of predictive
computational tools for RNA-protein interactions is slowing the development of potentially life-saving efforts
that either target or repurpose these interactions to address human disease. This proposal brings together four
labs to resolve this bottleneck, building on our recent studies that have achieved – all for the first time – blind
protein-RNA structure predictions reaching near-atomic resolution, large-scale prediction of protein-RNA
binding energetics with accuracy and precision of better than 1 kcal/mol, and redesign of a complex protein-
RNA interface to accurately retarget silencing complexes in vivo. We propose herein to unify, rigorously test,
and disseminate our labs’ methods to tackle three separate but synergistic computational problems in protein-
RNA research: automated correction of errors that pervade experimental protein-RNA complex structures (Aim
1), our richest resources of protein-RNA information; prediction of impacts of mutation on RNA-protein
interaction energetics (Aim 2) that could highlight new regulatory links in disease-associated alleles; and
design of novel engineered RNA-protein interactions (Aim 3) to facilitate rational perturbation of genetic events
to aid biological inquiry and eventually to ameliorate disease. We will evaluate success within each of our Aims
through true blind predictions tested through rapidly emerging cryoelectron microscopy maps and repurposed
sequencers that can measure hundreds of thousands of RNA-protein affinities in single experiments and, more
broadly, by adoption of our Rosetta software and online tools by the general biomedical research community.
The proposed protein-RNA-focused research addresses an area of molecular modeling that has received
surprisingly little attention in the computational community but is unambiguously important for accelerating
biological understanding and molecular medicine.

## Key facts

- **NIH application ID:** 10013238
- **Project number:** 5R01GM121487-04
- **Recipient organization:** FRED HUTCHINSON CANCER RESEARCH CENTER
- **Principal Investigator:** Philip Bradley
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $300,244
- **Award type:** 5
- **Project period:** 2017-09-15 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10013238

## Citation

> US National Institutes of Health, RePORTER application 10013238, High-resolution modeling of protein-RNA interfaces (5R01GM121487-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10013238. Licensed CC0.

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