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.
The diversity of
RNA structural elements and their documented role
in human diseases make RNA an attractive therapeutic target. However,
progress in drug discovery and development has been hindered by challenges
in the determination of high-resolution RNA structures and a limited
understanding of the parameters that drive RNA recognition by small
molecules, including a lack of validated quantitative structure–activity
relationships (QSARs). Herein, we develop QSAR models that quantitatively
predict both thermodynamic- and kinetic-based binding parameters of
small molecules and the HIV-1 transactivation response (TAR) RNA model
system. Small molecules bearing diverse scaffolds were screened against
TAR using surface plasmon resonance. Multiple linear regression (MLR)
combined with feature selection afforded robust models that allowed
direct interpretation of the properties critical for both binding
strength and kinetic rate constants. These models were validated with
new molecules, and their accurate performance was confirmed via comparison
to ensemble tree methods, supporting the general applicability of
this platform.
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