Exploring Secondary Structure Predictions for RNA-Targeted Drug Discovery: Power and Challenges

RNAs are increasingly recognized as promising drug targets, as both coding and noncoding RNAs act as key regulators in disease-related biological processes. However, a significant gap persists between the number of known RNA sequences and the solved RNA structures, posing a major bottleneck for RNA-targeted drug discovery. RNA secondary structure prediction offers the potential to facilitate the identification of druggable sites in novel RNA sequences by rapidly predicting base pairing patterns. In this study, we benchmarked widely used RNA secondary structure prediction tools against a newly curated dataset of ligand-bound RNA structures. We found that most tools achieve reasonable accuracy for RNAs with short sequences and simple motifs, but their performance declines for longer RNAs and those containing pseudoknots. Notably, prediction accuracy is reduced within ligand binding sites, where noncanonical base pairs and complex secondary structure elements are prevalent yet consistently unrecognized by the tools. Consequently, RNA ligand binding sites are poorly reconstructed by secondary structure predictions. This work provides the first comprehensive assessment of RNA secondary structure prediction for ligand-bound RNAs and demonstrates the challenges for integrating these methods into RNA-targeted drug discovery pipelines.