SONAR Discovers RNA-Binding Proteins from Analysis of Large-Scale Protein-Protein Interactomes

Kristopher W. Brannan, Wenhao Jin, Stephanie C. Huelga, Charles A.S. Banks, Joshua M. Gilmore, Laurence Florens, Michael P. Washburn, Eric L. Van Nostrand, Gabriel A. Pratt, Marie K. Schwinn, Danette L. Daniels, Gene W. Yeo

Research output: Contribution to journalArticlepeer-review

Abstract

RNA metabolism is controlled by an expanding, yet incomplete, catalog of RNA-binding proteins (RBPs), many of which lack characterized RNA binding domains. Approaches to expand the RBP repertoire to discover non-canonical RBPs are currently needed. Here, HaloTag fusion pull down of 12 nuclear and cytoplasmic RBPs followed by quantitative mass spectrometry (MS) demonstrates that proteins interacting with multiple RBPs in an RNA-dependent manner are enriched for RBPs. This motivated SONAR, a computational approach that predicts RNA binding activity by analyzing large-scale affinity precipitation-MS protein-protein interactomes. Without relying on sequence or structure information, SONAR identifies 1,923 human, 489 fly, and 745 yeast RBPs, including over 100 human candidate RBPs that contain zinc finger domains. Enhanced CLIP confirms RNA binding activity and identifies transcriptome-wide RNA binding sites for SONAR-predicted RBPs, revealing unexpected RNA binding activity for disease-relevant proteins and DNA binding proteins.

Original languageEnglish (US)
Pages (from-to)282-293
Number of pages12
JournalMolecular Cell
Volume64
Issue number2
DOIs
StatePublished - Oct 20 2016

Keywords

  • machine-learning
  • protein-protein interaction networks
  • RNA-binding proteins
  • support vector machine

ASJC Scopus subject areas

  • Molecular Biology
  • Cell Biology

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