Partner-Conditioned RNA Representations for Full-Sequence snoRNA–Target Interaction Prediction

Predicting small nucleolar RNA (snoRNA) interactions requires identifying both the target RNA and its interaction site among many sequence-compatible candidates. Existing supervised snoRNA formulations largely score preselected local regions, while interaction predictors based on RNA language models (RNA-LMs) typically combine representations obtained independently for each RNA. We introduce SnoBench, an experimentally grounded benchmark that extends evaluation from controlled ranking of predefined candidate windows to target identification and site localization from complete target sequences. Built from transcriptome-wide RNA-RNA contact measurements, SnoBench contains 9,156 snoRNA-target pairs and 31,790 observed interaction regions, preserves variable-width annotations, keeps closely related targets within the same data split, and samples cross-target controls from a genome-wide target pool. We further develop a partner-conditioned interaction model that adapts frozen pretrained RNA representations to the candidate partner before interaction scoring. Bidirectional cross-attention allows the two RNA representations to inform one another, while a signed nucleotide-pair attention bias uses biologically motivated base-pairing information to guide cross-RNA attention. Granularity-matched prediction heads separately model target-level compatibility and site-level interaction evidence. Across representative thermodynamic, snoRNA-specific, and RNA-LM-based baselines, our model performs best in both evaluation settings. In full-sequence prediction with 100 candidate targets, it improves Target Recall@5 from 10.34% to 15.72% and Target MRR from 0.0911 to 0.1183 over the strongest baseline, while more than doubling Joint Recall@5, which requires correct target identification and site localization. SnoBench further reveals that strong discrimination among predefined local candidates does not necessarily transfer to complete sequences.