RiboGRAM learns multi-scale non-coding RNA grammar for structural and regulatory inference

Le résumé fourni par la source

Protein-coding sequences are read through the triplet genetic code, whereas non-coding RNA sequences encode function through motifs, their positioning, and base-paired architectures. Here, we identify a non-coding RNA grammar defined by multi-scale motif usage and 5'-to-3' positional gradients, and develop RiboGRAM to learn this organization. Across nine organisms, non-coding RNAs showed scale-dependent divergence from coding sequences, stop-codon-like enrichment and directional U-rich and AU-rich gradients. Integrating multi-scale sequence encoding with contextual modeling, RiboGRAM organized RNA families, revealing motif-associated and pair-like patterns across layers. Pretraining on non-coding RNA yielded stronger zero-shot base-pair recovery than coding or messenger RNA pretraining, with these embeddings supporting alignment-free diffusion-based 3D modeling of short RNAs. In human RNA–protein binding profiles, RiboGRAM identified position-resolved motif organization across protein targets and cell types, with sequence-preference changes associated with reduced cross-cell transfer. Together, these results establish a learnable grammar of non-coding RNA sequence organization for structural and regulatory inference.

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