Evidence map›Paper›PMID 37421941›Full record

ArticleMolecular cell2023

HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence.

Wenhao Jin, Kristopher W Brannan, Katannya Kapeli, Samuel S Park, Hui Qing Tan, Maya L Gosztyla, Mayuresh Mujumdar, Joshua Ahdout, Bryce Henroid, Katherine Rothamel and 3 more

Abstract read
In one paragraph

Article in Molecular cell, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed.

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  17. RNA triggers chronic stress during neuronal aging.bioRxiv : the preprint server for biology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Wenhao JinDepartment of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
Kristopher W BrannanDepartment of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
Katannya KapeliDepartment of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Samuel S ParkDepartment of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
Hui Qing TanDepartment of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Maya L GosztylaDepartment of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
Mayuresh MujumdarDepartment of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
Joshua AhdoutDepartment of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
Bryce HenroidDepartment of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
Katherine RothamelDepartment of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
Joy S XiangDepartment of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
Limsoon WongDepartment of Computer Science, National University of Singapore, Singapore, Singapore.
Gene W YeoDepartment of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA. Electronic address: geneyeo@ucsd.edu.

Funding

Functional RNA elements in the human genomeR01HG004659 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Kevin Daniel Corbett, Eugene Wei-Ming Yeo · 2008 to 2026
$12.4M
A comprehensive binding and functional map of human RNA-binding proteinsU24HG009889 · NHGRI · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI GRAVELEY, BRENTON R., YEO, EUGENE WEI-MING · 2022 to 2025
$5.5M
Exploring functional complexes and disease networks within human RNA-binding protein interactomesK22NS112678 · NINDS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BRANNAN, KRISTOPHER · 2019 to 2024
$998k
NHGRI NIH HHS R01 HG004659NHGRI NIH HHS U24 HG009889NINDS NIH HHS K22 NS112678
6 · The paper itself

Abstract

RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains (RBDs). Here, we present a hybrid ensemble RBP classifier (HydRA), which leverages information from both intermolecular protein interactions and internal protein sequence patterns to predict RNA-binding capacity with unparalleled specificity and sensitivity using support vector machines (SVMs), convolutional neural networks (CNNs), and Transformer-based protein language models. Occlusion mapping by HydRA robustly detects known RBDs and predicts hundreds of uncharacterized RNA-binding associated domains. Enhanced CLIP (eCLIP) for HydRA-predicted RBP candidates reveals transcriptome-wide RNA targets and confirms RNA-binding activity for HydRA-predicted RNA-binding associated domains. HydRA accelerates construction of a comprehensive RBP catalog and expands the diversity of RNA-binding associated domains.

Indexed as

Deep LearningHydraAnimalsBinding SitesHumansProtein BindingRNARNAmachine learningprotein-protein interaction networkRNA-binding domainsRNA-binding proteins

Identifiers

PMID37421941
PMCPMC11098078

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.