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

Nature Biotechnology, ISSN: 1546-1696, Vol: 32, Issue: 6, Page: 562-568
2014
  • 183
    Citations
  • 0
    Usage
  • 496
    Captures
  • 0
    Mentions
  • 96
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    183
  • Captures
    496
  • Social Media
    96
    • Shares, Likes & Comments
      96
      • Facebook
        96

Article Description

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 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. © 2014 Nature America, Inc. All rights reserved.

Bibliographic Details

Jason D. Buenrostro; Carlos L. Araya; Lauren M. Chircus; Curtis J. Layton; Michael P. Snyder; William J. Greenleaf; Howard Y. Chang

Springer Science and Business Media LLC

Biochemistry, Genetics and Molecular Biology; Chemical Engineering; Immunology and Microbiology; Engineering

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