Abstract
Defining the RNA target selectivity of the proteins regulating mRNA metabolism is a key issue in RNA biology. Here we present a novel use of principal component analysis (PCA) to extract the RNA sequence preference of RNA binding proteins. We show that PCA can be used to compare the changes in the nuclear magnetic resonance (NMR) spectrum of a protein upon binding a set of quasi-degenerate RNAs and define the nucleobase specificity. We couple this application of PCA to an automated NMR spectra recording and processing protocol and obtain an unbiased and high-throughput NMR method for the analysis of nucleobase preference in protein–RNA interactions. We test the method on the RNA binding domains of three important regulators of RNA metabolism.
Highlights
Understanding how RNA-binding proteins select target RNA sequences is key to explain specificity in RNA regulation, but is a challenging problem
The method was tested on the RNA Recognition Motif (RRM) domain of the yeast mRNA 3 end processing factor RNA15, a core component of the Cleavage Factor 1A (CF1A) complex [12,13]
The interaction between RNA15 and the 3’UTR of the nascent RNA chain is necessary for CF1A recruitment [14] and is mediated by an RRM domain located in the N-terminal segment of the protein
Summary
Understanding how RNA-binding proteins select target RNA sequences is key to explain specificity in RNA regulation, but is a challenging problem. In SIA, 1H{15N} correlation nuclear magnetic resonance (NMR) spectroscopy is used to compare the average binding affinity of a protein domain to different sets of quasidegenerate RNA pools [3]. The only difference between the four pools is the specified base and the comparison of the average binding affinities of the protein for the four pools reports directly on its nucleobase preference. The normalized values across the different peaks in one titration are averaged to obtain the final comparative SIA score [3]. These scores reflect the preference of the protein for one nucleobase versus another. Manual measurement and tabulation of peak shifts in SIA analysis is laborious, and the choice of the peaks to be evaluated can introduce bias [4]
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