RiboSphere: Learning Unified and Efficient Representations of RNA Structures

TL;DR

RiboSphere is a novel framework that learns discrete, motif-aware geometric representations of RNA structures using vector quantization and flow matching, enabling accurate reconstruction and transfer to related tasks.

Contribution

It introduces a unified method combining vector quantization with flow matching to model RNA structures as discrete motifs, improving structure reconstruction and transfer learning.

Findings

Achieves RMSD 1.25 Å and TM-score 0.84 in structure reconstruction

Discrete codes are enriched for specific RNA motifs

Transfers effectively to inverse folding and ligand binding prediction

Abstract

Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce. We introduce \emph{RiboSphere}, a framework that learns \emph{discrete} geometric representations of RNA by combining vector quantization with flow matching. Our design is motivated by the modular organization of RNA architecture: complex folds are composed from recurring structural motifs. RiboSphere uses a geometric transformer encoder to produce SE(3)-invariant (rotation/translation-invariant) features, which are discretized with finite scalar quantization (FSQ) into a finite vocabulary of latent codes. Conditioned on these discrete codes, a flow-matching decoder reconstructs atomic coordinates, enabling high-fidelity structure generation. We find that the learned code indices are enriched for specific RNA motifs, suggesting that the model captures motif-level compositional structure rather than acting as a purely compressive bottleneck. Across benchmarks, RiboSphere achieves strong performance in structure reconstruction (RMSD 1.25\,\AA, TM-score 0.84), and its pretrained discrete representations transfer effectively to inverse folding and RNA--ligand binding prediction, with robust generalization in data-scarce regimes.