Development of pattern recognition technique for classification of RNA secondary structures by small molecules

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Three-dimensional RNA structures are notoriously difficult to determine, and the link between secondary structure and RNA conformation is only beginning to be understood. In this dissertation, I will discuss the development of a pattern recognition technique which utilizes differential binding of a receptor to a known analyte to develop patterns of structural motifs, which are then used to evaluate unknown analytes. I applied this method to RNA structures for the first time, revealing guiding principles in RNA:small molecule recognition and supporting our long term goal of classifying unknown RNA conformations and even function. Specifically, an aminoglycoside receptor library was obtained with commercially available aminoglycosides and through simple synthetic approaches. A training set of highly defined RNA secondary structure sequences was identified by computational modeling. I incorporated benzofuranyl uridine, a fluorescent base analogue, into the secondary structure of interest prior to exposing the training set to the small molecule library and the fluorescence changes were used as input for PCA. RNA structures were differentiated based on the five canonical RNA secondary structure motifs. Additionally, unique secondary structures within the Trans-Activation Response element, fluoride riboswitch and pre-queuosine 1 riboswitch could be differentiated based on the position of the fluorophore using the training set clusters. This method provides insight into both the RNA topological and conformational elements and the small molecule ligand properties critical to RNA recognition.






Eubanks, Christopher (2018). Development of pattern recognition technique for classification of RNA secondary structures by small molecules. Dissertation, Duke University. Retrieved from


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