| Literature DB >> 27720645 |
Kristopher W Brannan1, Wenhao Jin2, Stephanie C Huelga1, Charles A S Banks3, Joshua M Gilmore3, Laurence Florens3, Michael P Washburn4, Eric L Van Nostrand1, Gabriel A Pratt1, Marie K Schwinn5, Danette L Daniels5, Gene W Yeo6.
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.Entities:
Keywords: RNA-binding proteins; machine-learning; protein-protein interaction networks; support vector machine
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Year: 2016 PMID: 27720645 PMCID: PMC5074894 DOI: 10.1016/j.molcel.2016.09.003
Source DB: PubMed Journal: Mol Cell ISSN: 1097-2765 Impact factor: 17.970