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Protein–RNA specificity by high-throughput principal component analysis of NMR spectra

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 n...

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Autores principales: Collins, Katherine M., Oregioni, Alain, Robertson, Laura E., Kelly, Geoff, Ramos, Andres
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4381048/
https://www.ncbi.nlm.nih.gov/pubmed/25586222
http://dx.doi.org/10.1093/nar/gku1372
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author Collins, Katherine M.
Oregioni, Alain
Robertson, Laura E.
Kelly, Geoff
Ramos, Andres
author_facet Collins, Katherine M.
Oregioni, Alain
Robertson, Laura E.
Kelly, Geoff
Ramos, Andres
author_sort Collins, Katherine M.
collection PubMed
description 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.
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spelling pubmed-43810482015-04-03 Protein–RNA specificity by high-throughput principal component analysis of NMR spectra Collins, Katherine M. Oregioni, Alain Robertson, Laura E. Kelly, Geoff Ramos, Andres Nucleic Acids Res Methods Online 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. Oxford University Press 2015-03-31 2015-01-13 /pmc/articles/PMC4381048/ /pubmed/25586222 http://dx.doi.org/10.1093/nar/gku1372 Text en © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Collins, Katherine M.
Oregioni, Alain
Robertson, Laura E.
Kelly, Geoff
Ramos, Andres
Protein–RNA specificity by high-throughput principal component analysis of NMR spectra
title Protein–RNA specificity by high-throughput principal component analysis of NMR spectra
title_full Protein–RNA specificity by high-throughput principal component analysis of NMR spectra
title_fullStr Protein–RNA specificity by high-throughput principal component analysis of NMR spectra
title_full_unstemmed Protein–RNA specificity by high-throughput principal component analysis of NMR spectra
title_short Protein–RNA specificity by high-throughput principal component analysis of NMR spectra
title_sort protein–rna specificity by high-throughput principal component analysis of nmr spectra
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4381048/
https://www.ncbi.nlm.nih.gov/pubmed/25586222
http://dx.doi.org/10.1093/nar/gku1372
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