ja6b11087_si_001.pdf (19.71 MB)
Small Molecule-Based Pattern Recognition To Classify RNA Structure
journal contribution
posted on 2016-12-08, 00:00 authored by Christopher
S. Eubanks, Jordan E. Forte, Gary J. Kapral, Amanda E. HargroveThree-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.