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

Wenhao Jin1, Kristopher W Brannan1, Katannya Kapeli2

  • 1Department 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.

Molecular Cell
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

We developed HydRA, a novel tool to identify RNA-binding proteins (RBPs) and their domains. HydRA accurately predicts RNA-binding capacity, expanding our understanding of gene regulation and disease.

Keywords:
RNA-binding domainsRNA-binding proteinsmachine learningprotein-protein interaction network

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • RNA-binding proteins (RBPs) are crucial regulators of gene expression, and their dysfunction is linked to human diseases.
  • Identifying RBPs and their RNA-binding domains (RBDs) is essential for understanding cellular processes.
  • Current methods struggle to identify RBPs lacking canonical RBDs, limiting comprehensive RBP cataloging.

Purpose of the Study:

  • To develop a highly specific and sensitive computational tool for predicting RNA-binding capacity.
  • To identify novel RNA-binding domains beyond canonical RBDs.
  • To accelerate the discovery of a comprehensive catalog of RBPs and their functions.

Main Methods:

  • Developed HydRA, a hybrid ensemble classifier integrating protein interaction networks and sequence patterns.
  • Utilized machine learning models including Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Transformer-based protein language models.
  • Employed occlusion mapping for domain detection and Enhanced CLIP (eCLIP) to validate RNA targets and binding activity.

Main Results:

  • HydRA demonstrates unparalleled specificity and sensitivity in predicting RNA-binding capacity.
  • Identified hundreds of uncharacterized RNA-binding associated domains.
  • eCLIP experiments validated HydRA's predictions, confirming RNA targets and binding activity for novel domains.

Conclusions:

  • HydRA significantly advances the identification of RBPs and their associated domains.
  • Expands the known diversity of RNA-binding associated domains, crucial for understanding gene regulation.
  • Provides a powerful tool for constructing comprehensive RBP catalogs and investigating RBP-related diseases.