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Small Molecule-Based Pattern Recognition To Classify RNA Structure.

Publication ,  Journal Article
Eubanks, CS; Forte, JE; Kapral, GJ; Hargrove, AE
Published in: Journal of the American Chemical Society
January 2017

Three-dimensional RNA structures are notoriously difficult to determine, and the link between secondary structure and RNA conformation is only beginning to be understood. These challenges have hindered the identification of guiding principles for small molecule:RNA recognition. We herein demonstrate that the strong and differential binding ability of aminoglycosides to RNA structures can be used to classify five canonical RNA secondary structure motifs through principal component analysis (PCA). In these analyses, the aminoglycosides act as receptors, while RNA structures labeled with a benzofuranyluridine fluorophore act as analytes. Complete (100%) predictive ability for this RNA training set was achieved by incorporating two exhaustively guanidinylated aminoglycosides into the receptor library. The PCA was then externally validated using biologically relevant RNA constructs. In bulge-stem-loop constructs of HIV-1 transactivation response element (TAR) RNA, we achieved nucleotide-specific classification of two independent secondary structure motifs. Furthermore, examination of cheminformatic parameters and PCA loading factors revealed trends in aminoglycoside:RNA recognition, including the importance of shape-based discrimination, and suggested the potential for size and sequence discrimination within RNA structural motifs. These studies present a new approach to classifying RNA structure and provide direct evidence that RNA topology, in addition to sequence, is critical for the molecular recognition of RNA.

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Published In

Journal of the American Chemical Society

DOI

EISSN

1520-5126

ISSN

0002-7863

Publication Date

January 2017

Volume

139

Issue

1

Start / End Page

409 / 416

Related Subject Headings

  • Small Molecule Libraries
  • RNA, Viral
  • Nucleic Acid Conformation
  • HIV Long Terminal Repeat
  • General Chemistry
  • 40 Engineering
  • 34 Chemical sciences
  • 03 Chemical Sciences
 

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Eubanks, C. S., Forte, J. E., Kapral, G. J., & Hargrove, A. E. (2017). Small Molecule-Based Pattern Recognition To Classify RNA Structure. Journal of the American Chemical Society, 139(1), 409–416. https://doi.org/10.1021/jacs.6b11087
Eubanks, Christopher S., Jordan E. Forte, Gary J. Kapral, and Amanda E. Hargrove. “Small Molecule-Based Pattern Recognition To Classify RNA Structure.Journal of the American Chemical Society 139, no. 1 (January 2017): 409–16. https://doi.org/10.1021/jacs.6b11087.
Eubanks CS, Forte JE, Kapral GJ, Hargrove AE. Small Molecule-Based Pattern Recognition To Classify RNA Structure. Journal of the American Chemical Society. 2017 Jan;139(1):409–16.
Eubanks, Christopher S., et al. “Small Molecule-Based Pattern Recognition To Classify RNA Structure.Journal of the American Chemical Society, vol. 139, no. 1, Jan. 2017, pp. 409–16. Epmc, doi:10.1021/jacs.6b11087.
Eubanks CS, Forte JE, Kapral GJ, Hargrove AE. Small Molecule-Based Pattern Recognition To Classify RNA Structure. Journal of the American Chemical Society. 2017 Jan;139(1):409–416.
Journal cover image

Published In

Journal of the American Chemical Society

DOI

EISSN

1520-5126

ISSN

0002-7863

Publication Date

January 2017

Volume

139

Issue

1

Start / End Page

409 / 416

Related Subject Headings

  • Small Molecule Libraries
  • RNA, Viral
  • Nucleic Acid Conformation
  • HIV Long Terminal Repeat
  • General Chemistry
  • 40 Engineering
  • 34 Chemical sciences
  • 03 Chemical Sciences