Sequence-Informed Geometric Evaluation of RNA 3D Structures
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Abstract (arXiv)
Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator that conditions structural representations on nucleotide embeddings from a pretrained RNA language model. Early results show that SIRGE outperforms established evaluators in Kendall--τ alignment, Top-1 selection, and Top-3 ranking. Controlled comparisons further show that sequence conditioning corrects errors made by an otherwise matched geometric model and improves target-level rank structure. These findings provide initial evidence that pretrained sequence representations supply ranking information that complements geometric reasoning.
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References · 4
- 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 204
- 2020Predicting What You Already Know Helps: Provable Self-Supervised Learningcited by 152
- 2024RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Taskscited by 33
- 2023A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systemscited by 10
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- 2024RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching
- 2026La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching
- 2026TANGO: RNA Topology and Geometry Co-Design
- 2026RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion