Defining RNA–Small Molecule Affinity Landscapes Enables Design of a Small Molecule Inhibitor of an Oncogenic Noncoding RNA

Abstract

RNA drug targets are pervasive in cells, but methods to design small molecules that target them are sparse. Herein, we report a general approach to score the affinity and selectivity of RNA motif–small molecule interactions identified via selection. Named High Throughput Structure–Activity Relationships Through Sequencing (HiT-StARTS), HiT-StARTS is statistical in nature and compares input nucleic acid sequences to selected library members that bind a ligand via high throughput sequencing. The approach allowed facile definition of the fitness landscape of hundreds of thousands of RNA motif–small molecule binding partners. These results were mined against folded RNAs in the human transcriptome and identified an avid interaction between a small molecule and the Dicer nuclease-processing site in the oncogenic microRNA (miR)-18a hairpin precursor, which is a member of the miR-17-92 cluster. Application of the small molecule, Targapremir-18a, to prostate cancer cells inhibited production of miR-18a from the cluster, de-repressed serine/threonine protein kinase 4 protein (STK4), and triggered apoptosis. Profiling the cellular targets of Targapremir-18a via Chemical Cross-Linking and Isolation by Pull Down (Chem-CLIP), a covalent small molecule–RNA cellular profiling approach, and other studies showed specific binding of the compound to the miR-18a precursor, revealing broadly applicable factors that govern small molecule drugging of noncoding RNAs.

Approved Anti-cancer Drugs Target Oncogenic Non-coding RNAs

“…HiT-StARTS quickly identifies binding and non-binding RNA motifs for a specific small molecule and minimizes false negatives and positives. Binding assays were completed between selected RNA motif-small molecule pairs, revealing high affinity and selective binding when Z obs >8 (Table 1), as observed previously (Velagapudi, et al, 2017) and highlighting the predictive value of HiT-StARTS. Fitness Scores are assigned for RNA binders by normalizing Z obs values to the highest Z obs for a given selection, with the best score assigned a value of 100 (Velagapudi, et al, 2014).…”

A Massively Parallel Selection of Small Molecule-RNA Motif Binding Partners Informs Design of an Antiviral from Sequence

“…We have previously validated the secondary structures formed by members of RNA libraries by enzymatic mapping. 8,12,29 These discrete RNA motifs were selected because they are present in cellular RNAs. Because of the library-versus-library nature of these studies, they represent the largest screens completed to date, probing >52,000,000 interactions.…”

“…In myriad studies, 2DCS has defined selective RNA motif-small molecule interactions with varied affinities. 11,12,30,31 …”

“…We have previously shown that at least 6-fold coverage is required to generate binding landscape maps. 12 RNA-seq data were then analyzed by high throughput structure-activity relationships through sequencing (HiT-StARTS) 12 to identify the privileged RNA motifs that bind each small molecule. In brief, the frequency of occurrence of a selected RNA in RNA-seq data was compared with the frequency of occurrence of the same RNA in RNA-seq analysis of the starting RNA library to account for biases arising during transcription and RT-PCR.…”

“…10,11 By sequencing the RNAs that bind to a small molecule and are captured via 2DCS, the most avid interactions are defined, building an encyclopedia of privileged RNA motif-small molecule interactions that can inform drug design. 12 Much more data of this type are required to effectively target the myriad of disease-causing RNAs.…”