Novel Insights of Structure-Based Modeling for RNA-Targeted Drug Discovery

Substantial progress in RNA biology highlights the importance
of
RNAs (e.g., microRNAs) in diseases and the potential of targeting
RNAs for drug discovery. However, the lack of RNA-specific modeling
techniques demands the development of new tools for RNA-targeted rational
drug design. Herein, we implemented integrated approaches of accurate
RNA modeling and virtual screening for RNA inhibitor discovery with
the most comprehensive evaluation to date of five docking and 11 scoring
methods. For the first time, statistical analysis was heavily employed
to assess the significance of our predictions. We found that GOLD:GOLD
Fitness and rDock:rDock_solv could accurately predict the RNA ligand
poses, and ASP rescoring further improved the ranking of ligand binding
poses. Due to the weak correlations (R2 < 0.3) of existing scoring with experimental binding affinities,
we implemented two new RNA-specific scoring functions, iMDLScore1
and iMDLScore2, and obtained better correlations with R2 = 0.70 and 0.79, respectively. We also proposed a multistep
virtual screening approach and demonstrated that rDock:rDock_solv
together with iMDLScore2 rescoring obtained the best enrichment on
the flexible RNA targets, whereas GOLD:GOLD Fitness combined with
rDock_solv rescoring outperformed other methods for rigid RNAs. This
study provided practical strategies for RNA modeling and offered new
insights into RNA–small molecule interactions for drug discovery.