Cross-Virus RNA Language Models for Within-Family RNA-RNA Interaction Prediction in SARS-CoV-2 and Porcine Deltacoronavirus.

RNA large language models (RNA LLMs) show potential for predicting viral RNA-RNA interactions (RRIs), but their ability to transfer between viruses remains unclear. Here, we evaluated three nucleotide language models using vRIC-seq-derived RRI datasets from two members of the Coronaviridae family, SARS-CoV-2 and porcine deltacoronavirus (PDCoV). We compared within-virus five-fold cross-validation with bidirectional cross-virus testing. DNABERT achieved within-virus AUC values of 0.9595 for PDCoV and 0.9741 for SARS-CoV-2. Under cross-virus transfer, its AUC decreased to 0.8372 when trained on PDCoV and tested on SARS-CoV-2 and to 0.8116 in the reverse direction, with corresponding F1 scores of 0.7847 and 0.7617. RNAErnie retained similar but slightly lower cross-virus discrimination, whereas the Nucleotide Transformer approached random discrimination. These results provide a two-virus proof of concept that sequence-based models can retain partially shared discriminative signals across coronavirus genera. They do not establish functional, tropism-related, or receptor-dependent transferability, and broader evaluation across additional viruses and orthogonal structural validation will be required before application to emerging-virus screening.