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RiboSphere: Learning Unified and Efficient Representations of RNA Structures

arXiv:2603.19636v1 Announce Type: new 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

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Zhou Zhang, Hanqun Cao, Cheng Tan, Fang Wu, Pheng Ann Heng, Tianfan Fu · · 1 min read · 4 views
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arXiv:2603.19636v1 Announce Type: new 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.

Executive Summary

The article presents RiboSphere, a novel framework for learning unified and efficient representations of RNA structures. By combining vector quantization with flow matching, RiboSphere produces SE(3)-invariant features that are discretized into finite scalar quantization (FSQ) latent codes. The model achieves strong performance in structure reconstruction and exhibits robust generalization in data-scarce regimes. The learned code indices are enriched for specific RNA motifs, suggesting that the model captures motif-level compositional structure. The framework has several applications in RNA structure prediction, inverse folding, and RNA-ligand binding prediction. The results demonstrate the potential of RiboSphere in improving our understanding of RNA structures and their functions.

Key Points

  • ▸ RiboSphere combines vector quantization with flow matching to learn discrete geometric representations of RNA structures.
  • ▸ The framework uses a geometric transformer encoder to produce SE(3)-invariant features.
  • ▸ RiboSphere achieves strong performance in structure reconstruction and exhibits robust generalization in data-scarce regimes.

Merits

Strength

RiboSphere's ability to capture motif-level compositional structure is a significant advantage over existing RNA structure prediction methods.

Transferability

The pretrained discrete representations of RiboSphere transfer effectively to inverse folding and RNA-ligand binding prediction tasks.

Demerits

Limitation

The framework may not generalize well to highly complex or atypical RNA structures.

Computational Cost

The computational cost of training and evaluating RiboSphere may be high due to the complexity of the framework.

Expert Commentary

The article presents a novel framework for RNA structure prediction that demonstrates significant promise. However, the framework's limitations and potential applications warrant further investigation. The ability of RiboSphere to capture motif-level compositional structure is a significant advantage, but its generalizability to complex or atypical RNA structures is unclear. Moreover, the computational cost of training and evaluating RiboSphere may be high, which could impact its adoption in practical applications. Nevertheless, the results demonstrate the potential of RiboSphere in improving our understanding of RNA structures and their functions.

Recommendations

  • ✓ Further investigation is needed to determine the generalizability of RiboSphere to complex or atypical RNA structures.
  • ✓ The computational cost of training and evaluating RiboSphere should be optimized to improve its practical applications.

Sources

Original: arXiv - cs.LG

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