An RNA Language Model trained on sequence alone reveals the structural logic of Internal Ribosome Entry Sites

Abstract

Millions of RNA sequences are readily available, but the structures that determine their function are not. Picornaviruses initiate translation through Internal Ribosome Entry Sites (IRESes), RNA elements that recruit ribosomes independent of the 5` cap. These elements are large and highly divergent, and structural understanding remains limited to a handful of cases. We address this bottleneck by introducing an RNA language model (Albatross), trained purely on sequence, that predicts high-quality IRES structures at scale. We collect in cellulo chemical probing data for 96 full-length IRESes from divergent viruses and show that Albatross achieves far higher precision (0.80) than state-of-the-art predictions (0.47). Analyzing 75,000 IRES structures, we discover a novel Type II structural subclass and validate it experimentally. We demonstrate the pipeline broadly generalizes to identify functional structures, including tertiary contacts and alternative riboswitch structures. These findings establish Albatross as a scalable framework that accelerates RNA structure discovery and antiviral targeting.