Small Molecule-Based Pattern Recognition To Classify RNA Structure.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Eubanks, CS; Forte, JE; Kapral, GJ; Hargrove, AE

Published Date

  • January 2017

Published In

Volume / Issue

  • 139 / 1

Start / End Page

  • 409 - 416

PubMed ID

  • 28004925

Pubmed Central ID

  • PMC5465965

Electronic International Standard Serial Number (EISSN)

  • 1520-5126

International Standard Serial Number (ISSN)

  • 0002-7863

Digital Object Identifier (DOI)

  • 10.1021/jacs.6b11087


  • eng