Differentiation and classification of RNA motifs using small molecule-based pattern recognition.

Understanding how to design small molecules that target coding and non-coding RNA has the potential to exponentially increase the number of therapeutically-relevant druggable targets, which are currently mostly proteins. However, there is limited information on the principles at the basis of RNA recognition. In this chapter, we describe a pattern-based technique that can be used for the simultaneous elucidation of RNA motifs and small molecule features for RNA selective recognition, termed Pattern Recognition of RNA by Small Molecules (PRRSM). We provide protocols for the computational design and synthetic preparation of an RNA training set as well as how to perform the assay in plate reader format. Furthermore, we provide details on how to perform and interpret the statistical analysis and indicate possible future extensions of the technique. By combining insights into characteristics of the small molecules and of the RNA that leads to differentiation, PRRSM promises to accelerate the elucidation of the determinants at the basis of RNA recognition.

Visualizing RNA Conformational Changes via Pattern Recognition of RNA by Small Molecules.

Conformational changes in RNA play vital roles in the regulation of many biological systems, yet these changes can be challenging to visualize. Previously, we demonstrated that Pattern Recognition of RNA by Small Molecules (PRRSM) can unbiasedly cluster defined RNA secondary structure motifs utilizing an aminoglycoside receptor library. In this work, we demonstrate the power of this method to visualize changes in folding at the secondary structure level within two distinct riboswitch structures. After labeling at three independent positions on each riboswitch, PRRSM accurately classified all apo and ligand-bound riboswitch structures, including changes in the size of a structural motif, and revealed modification sites that prevented folding and/or led to a mixture of states. These data underscore the utility and robustness of the PRRSM assay for rapid assessment of RNA structural changes and for gaining ready insight into nucleotide positions critical to RNA folding.

RNA Structural Differentiation: Opportunities with Pattern Recognition.

Our awareness and appreciation of the many regulatory roles of RNA have dramatically increased in the past decade. This understanding, in addition to the impact of RNA in many disease states, has renewed interest in developing selective RNA-targeted small molecule probes. However, the fundamental guiding principles in RNA molecular recognition that could accelerate these efforts remain elusive. While high-resolution structural characterization can provide invaluable insight, examples of well-characterized RNA structures, not to mention small molecule:RNA complexes, remain limited. This Perspective provides an overview of the current techniques used to understand RNA molecular recognition when high-resolution structural information is unavailable. We will place particular emphasis on a new method, pattern recognition of RNA with small molecules (PRRSM), that provides rapid insight into critical components of RNA recognition and differentiation by small molecules as well as into RNA structural features.

Sensing the impact of environment on small molecule differentiation of RNA sequences.

Using pattern recognition of RNA with small molecules (PRRSM) with fluorescent RNA chemosensors and aminoglycosides, we reveal the impact of changing environmental conditions on the differentiation of a range of RNA structures as well as the ability to predict different sequence/size compositions of five canonical RNA motifs.

Amiloride as a new RNA-binding scaffold with activity against HIV-1 TAR.

Diversification of RNA-targeted scaffolds offers great promise in the search for selective ligands of therapeutically relevant RNA such as HIV-1 TAR. We herein report the establishment of amiloride as a novel RNA-binding scaffold along with synthetic routes for combinatorial C(5)- and C(6)-diversification. Iterative modifications at the C(5)- and C(6)- positions yielded derivative 24, which demonstrated a 100-fold increase in activity over the parent dimethylamiloride in peptide displacement assays. NMR chemical shift mapping was performed using the 2D SOFAST- [1H-13C] HMQC NMR method, which allowed for facile and rapid evaluation of binding modes for all library members. Cheminformatic analysis revealed distinct differences between selective and non-selective ligands. In this study, we evolved dimethylamiloride from a weak TAR ligand to one of the tightest binding selective TAR ligands reported to date through a novel combination of synthetic methods and analytical techniques. We expect these methods to allow for rapid library expansion and tuning of the amiloride scaffold for a range of RNA targets and for SOFAST NMR to allow unprecedented evaluation of small molecule:RNA interactions.

Small Molecule-Based Pattern Recognition To Classify RNA Structure.

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.