Adaptive 2.5D base-pairing subgraph search detects RNA small-molecule binding sites
TL;DR
Ribo-LENS turns coarse base-pairing structure into a practical entry point for screening the vast, largely unexplored RNA target space, and depends far less on sequence homology than competing predictors.
Abstract
Ribo-LENS is a geometric deep-learning framework for detecting small-molecule binding sites in RNA structures. It is designed to exploit two properties of RNA base-pairing networks: their robustness to conformational fluctuation and the functional signatures they encode. By reasoning directly in the space of base-pairing subgraphs, Ribo-LENS assembles coherent binding sites, in contrast to methods that score residues independently. In extensive experiments, Ribo-LENS is competitive with, and often outperforms, large all-atom co-folding models (AlphaFold3, Chai-1), fine-tuned language models (GerNA-Bind), and structure-based tools (RNAsite), raising mean MCC to 0.380 (versus 0.321 for the state-of-the-art GerNA-Bind). It is strongly robust to apo/holo rearrangement, with its accuracy tracking the base-pairing graph (Spearman ρ = 0.82 with binding-site graph edit distance) rather than backbone displacement (ρ = −0.15 with RMSD), and depends far less on sequence homology than competing predictors. In an end-to-end, sequence-based virtual screen of the ROBIN assay (∼25,000 compounds), Ribo-LENS guides docking to a small predicted pocket, matching a blind all-atom cavity search (enrichment factors up to 6.1) at a fraction of the search cost; on two SARS-CoV-2 targets its predicted sites align with NMR chemical-shift perturbations. Ribo-LENS turns coarse base-pairing structure into a practical entry point for screening the vast, largely unexplored RNA target space.