# Post-transcriptional Regulatory Networks

> **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2024 · $643,713

## Abstract

RNA-binding proteins (RBPs) play key roles in RNA splicing, editing, nuclear export, translation, turnover, and
subcellular localization. Reflecting their importance, RBPs and their cis-regulatory elements (CREs) have
broad implications in human health: mutations in RBPs or CREs have well-established roles in cancer,
developmental defects, particularly in neural development, and in neural degenerative diseases.
Using a combination of a high-throughput, in-vitro-selection-based RNA binding assay, RNAcompete, and
machine learning (ML) models trained to map from an RBP’s protein sequence to its RNA binding preferences,
this project will endeavor to assign RNA sequence- and structural-context binding preferences to all human
RBPs, all vertebrate RBPs, and the vast majority of metazoan RBPs. These specificities will then be used to
detect and assign function to RBPs and cis-regulatory elements (CREs) in human genomes, as well as those
of other model organisms. The specificities, machine learning models, and predicted CREs will be distributed
widely via publication, open-source software, and user-friendly web tools like cisBP-RNA.
This project has the potential to transform cancer and human genetics research supporting the estimation of
the functional impact of germline or somatic mutations on post-transcriptional regulation (PTR). By improving
the reconstruction of PTR networks, this project will speed research in this emerging field toward a complete
understanding of this key process. This project will also permit the study of the evolution of PTR by developing
tools to reconstruct PTR networks in other organisms based solely on genomic and transcriptomic data.
RNAcompete will be used to assess the RNA sequence-binding preferences of the 511 still-uncharacterized
RBPs in humans and D. rerio (zebrafish), thereby establishing a complete catalog of binding preferences for all
likely sequence-specific RBPs in these two species. These data will be combined with binding data for >500
other RBPs from a variety of sources and used to train an ML model that reconstructs RNA-binding
preferences given RBP protein sequences. These models will also leverage recent advances in de novo
prediction of protein structure from sequence. RBPs will be assigned roles in PTR based on (i) the location and
conservation, in human transcripts of their predicted target CREs, (ii) the correlation of their expression with
the PTR fate of their putative target transcripts, and (iii) other, more powerful regression methods like the
Inferelator. CRE predictions will be continuously improved using in vivo data to recalibrate in vitro motif models
and to improve in silico predictions of transcript RNA secondary structure. Our predicted CREs and
reconstructed PTR networks will be validated by comparisons with in vivo data collected by our team and
others.

## Key facts

- **NIH application ID:** 10899590
- **Project number:** 5R01HG013328-02
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Timothy Hughes
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $643,713
- **Award type:** 5
- **Project period:** 2023-08-04 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10899590

## Citation

> US National Institutes of Health, RePORTER application 10899590, Post-transcriptional Regulatory Networks (5R01HG013328-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10899590. Licensed CC0.

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