Abstract

Thousands of RNA-binding proteins (RBPs) crosslink to cellular mRNA. Among these are numerous unconventional RBPs (ucRBPs)—proteins that associate with RNA but lack known RNA-binding domains (RBDs). The vast majority of ucRBPs have uncharacterized RNA-binding specificities. We analyzed 492 human ucRBPs for intrinsic RNA-binding in vitro and identified 23 that bind specific RNA sequences. Most (17/23), including 8 ribosomal proteins, were previously associated with RNA-related function. We identified the RBDs responsible for sequence-specific RNA-binding for several of these 23 ucRBPs and surveyed whether corresponding domains from homologous proteins also display RNA sequence specificity. CCHC-zf domains from seven human proteins recognized specific RNA motifs, indicating that this is a major class of RBD. For Nudix, HABP4, TPR, RanBP2-zf, and L7Ae domains, however, only isolated members or closely related homologs yielded motifs, consistent with RNA-binding as a derived function. The lack of sequence specificity for most ucRBPs is striking, and we suggest that many may function analogously to chromatin factors, which often crosslink efficiently to cellular DNA, presumably via indirect recruitment. Finally, we show that ucRBPs tend to be highly abundant proteins and suggest their identification in RNA interactome capture studies could also result from weak nonspecific interactions with RNA.

Details

Title
RNA-binding proteins that lack canonical RNA-binding domains are rarely sequence-specific
Author
Ray, Debashish 1 ; Laverty, Kaitlin U. 2 ; Jolma, Arttu 1 ; Nie, Kate 2 ; Samson, Reuben 2 ; Pour, Sara E. 2 ; Tam, Cyrus L. 3 ; von Krosigk, Niklas 2 ; Nabeel-Shah, Syed 2 ; Albu, Mihai 1 ; Zheng, Hong 1 ; Perron, Gabrielle 4   VIAFID ORCID Logo  ; Lee, Hyunmin 1 ; Najafabadi, Hamed 4 ; Blencowe, Benjamin 2   VIAFID ORCID Logo  ; Greenblatt, Jack 2 ; Morris, Quaid 5   VIAFID ORCID Logo  ; Hughes, Timothy R. 2   VIAFID ORCID Logo 

 University of Toronto, Donnelly Centre, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938) 
 University of Toronto, Donnelly Centre, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); University of Toronto, Department of Molecular Genetics, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938) 
 Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, USA (GRID:grid.51462.34) (ISNI:0000 0001 2171 9952); Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, USA (GRID:grid.5386.8) (ISNI:000000041936877X) 
 McGill University, Department of Human Genetics, Montréal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649); McGill Genome Centre, Montréal, Canada (GRID:grid.511986.2) 
 University of Toronto, Donnelly Centre, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); University of Toronto, Department of Molecular Genetics, Toronto, Canada (GRID:grid.17063.33) (ISNI:0000 0001 2157 2938); Memorial Sloan Kettering Cancer Center, Computational and Systems Biology Program, New York, USA (GRID:grid.51462.34) (ISNI:0000 0001 2171 9952); Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, USA (GRID:grid.5386.8) (ISNI:000000041936877X) 
Pages
5238
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2793281328
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.