Small molecule-based modulation of a triple helix in the long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) has been proposed as an attractive avenue for cancer treatment and a model system for understanding small molecule:RNA recognition. To elucidate fundamental recognition principles and structure–function relationships, we designed and synthesized nine novel analogs of a diphenylfuran-based small molecule DPFp8, a previously identified lead binder of MALAT1. We investigated the role of recognition modalities in binding and in silico studies along with the relationship between affinity, stability and in vitro enzymatic degradation of the triple helix. Specifically, molecular docking studies identified patterns driving affinity and selectivity, including limited ligand flexibility, as observed by ligand preorganization and 3D shape complementarity for the binding pocket. The use of differential scanning fluorimetry allowed rapid evaluation of ligand-induced thermal stabilization of the triple helix, which correlated with decreased in vitro degradation of this structure by the RNase R exonuclease. The magnitude of stabilization was related to binding mode and selectivity between the triple helix and its precursor stem loop structure. Together, this work demonstrates the value of scaffold-based libraries in revealing recognition principles and of raising broadly applicable strategies, including functional assays, for small molecule–RNA targeting.

Summary Recent efforts to identify novel bacterial structured noncoding RNA (ncRNA) motifs through searching long, GC-rich intergenic regions (IGRs) have revealed several new classes, including the recently validated HMP-PP riboswitch. The DIMPL discovery pipeline described herein enables rapid extraction and selection of bacterial IGRs that are enriched for structured ncRNAs. Moreover, DIMPL automates the subsequent computational steps necessary for their functional identification. Availability and Implementation The DIMPL pipeline is freely available as a Docker image with an accompanying set of Jupyter notebooks. Full instructions for download and use are available at https://github.com/breakerlab/dimpl.