RiboSphere: Learning Unified and Efficient Representations of RNA Structures

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\,Å, 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.

Figures (5)

• Figure 1: Overview of RiboSphere: from continuous RNA geometry to discrete, interpretable structural units.
• Figure 2: Overall pipeline of RiboSphere. (a) General autoencoder pipeline. RNA atomic coordinates are encoded by a geometric transformer into continuous latent representations, which are discretized via Finite Scalar Quantization (FSQ) to obtain discrete geometric tokens. A flow-matching decoder reconstructs full 3D structures from the discrete representations, enabling high-fidelity structure reconstruction and serving as the pretraining objective. (b) Multi-task downstream integration. The pretrained encoder and quantizer are frozen and reused across downstream tasks. Discrete structural tokens and continuous embeddings are transferred to task-specific architectures for inverse folding and RNA--ligand binding prediction, providing a shared and interpretable geometric backbone.
• Figure 3: Structural consistency of high-frequency VQ token sequences.
• Figure 4: Motif-Specific Token Distributions in the VQ Structural Space
• Figure 5: Recovery–Diversity trade-off for the inverse folding model under different sampling temperatures.