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Nat Biotechnol. 2014 Jun;32(6):562-8. doi: 10.1038/nbt.2880. Epub 2014 Apr 13.

Quantitative analysis of RNA-protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes.

Author information

1
1] Department of Genetics, Stanford University School of Medicine, Stanford, California, USA. [2] Program in Epithelial Biology and the Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA. [3] These authors contributed equally to this work.
2
1] Department of Genetics, Stanford University School of Medicine, Stanford, California, USA. [2] These authors contributed equally to this work.
3
1] Department of Genetics, Stanford University School of Medicine, Stanford, California, USA. [2] Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California, USA.
4
Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.
5
Program in Epithelial Biology and the Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA.

Abstract

RNA-protein interactions drive fundamental biological processes and are targets for molecular engineering, yet quantitative and comprehensive understanding of the sequence determinants of affinity remains limited. Here we repurpose a high-throughput sequencing instrument to quantitatively measure binding and dissociation of a fluorescently labeled protein to >10(7) RNA targets generated on a flow cell surface by in situ transcription and intermolecular tethering of RNA to DNA. Studying the MS2 coat protein, we decompose the binding energy contributions from primary and secondary RNA structure, and observe that differences in affinity are often driven by sequence-specific changes in both association and dissociation rates. By analyzing the biophysical constraints and modeling mutational paths describing the molecular evolution of MS2 from low- to high-affinity hairpins, we quantify widespread molecular epistasis and a long-hypothesized, structure-dependent preference for G:U base pairs over C:A intermediates in evolutionary trajectories. Our results suggest that quantitative analysis of RNA on a massively parallel array (RNA-MaP) provides generalizable insight into the biophysical basis and evolutionary consequences of sequence-function relationships.

PMID:
24727714
PMCID:
PMC4414031
DOI:
10.1038/nbt.2880
[Indexed for MEDLINE]
Free PMC Article
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