Global pairwise RNA interaction landscapes reveal core features of protein recognition

Qin Zhou, Nikesh Kunder, José Alberto De la Paz, Alexandra E. Lasley, Vandita D. Bhat, Faruck Morcos () and Zachary T. Campbell ()
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Qin Zhou: University of Texas at Dallas
Nikesh Kunder: University of Texas at Dallas
José Alberto De la Paz: University of Texas at Dallas
Alexandra E. Lasley: University of Texas at Dallas
Vandita D. Bhat: University of Texas at Dallas
Faruck Morcos: University of Texas at Dallas
Zachary T. Campbell: University of Texas at Dallas

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: Abstract RNA–protein interactions permeate biology. Transcription, translation, and splicing all hinge on the recognition of structured RNA elements by RNA-binding proteins. Models of RNA–protein interactions are generally limited to short linear motifs and structures because of the vast sequence sampling required to access longer elements. Here, we develop an integrated approach that calculates global pairwise interaction scores from in vitro selection and high-throughput sequencing. We examine four RNA-binding proteins of phage, viral, and human origin. Our approach reveals regulatory motifs, discriminates between regulated and non-regulated RNAs within their native genomic context, and correctly predicts the consequence of mutational events on binding activity. We design binding elements that improve binding activity in cells and infer mutational pathways that reveal permissive versus disruptive evolutionary trajectories between regulated motifs. These coupling landscapes are broadly applicable for the discovery and characterization of protein–RNA recognition at single nucleotide resolution.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04729-0

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DOI: 10.1038/s41467-018-04729-0

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