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<p>Determining RNA three-dimensional (3D) structure and conformers remains a grand challenge in structural biology, primarily owing to the scarcity of experimental data, the intrinsic flexibility of RNA molecules, and the limitations of current experimental and computational methods. Here we propose trRosettaRNA2, a deep learning-based end-to-end approach to this problem. Considering the scarcity of RNA 3D structure data, trRosettaRNA2 integrates an auxiliary secondary structure (SS) prior module, pre-trained on extensive SS data, to generate informative base-pairing priors. This module also serves as an independent RNA SS prediction method, trRNA2-SS, and achieves state-of-the-art performance. To enable end-to-end prediction, trRosettaRNA2 uses SS-aware attention to generate RNA 3D structure and conformers (distinct 3D spatial arrangements of the same molecule resulting from its intrinsic flexibility). Rigorous benchmarks demonstrate that trRosettaRNA2 outperforms other RNA 3D structure prediction methods, despite using substantially fewer parameters and computational resources. Notably, its flexibility in leveraging diverse secondary structure inputs provides a pathway to generate accurate 3D structure and explore the RNA conformers. Based on trRosettaRNA2, our group, Yang-Server, was the top automated server for RNA structure prediction in the CASP16 blind test, surpassing AlphaFold 3. This performance highlights that trRosettaRNA2 represents a solid step forward for RNA structure prediction. Application to the ribonuclease P RNA demonstrates that trRosettaRNA2 successfully captures its structural heterogeneity even without requiring experimental data, showing its potential to predict RNA conformational ensembles.</p>

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Predicting RNA 3D structure and conformers using a pre-trained secondary structure model and structure-aware attention

  • 期刊论文

  • 发表日期：

    2026年4月21日

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  • Wenkai Wang,
  • Zhenling Peng,
  • Jianyi Yang

摘要

Determining RNA three-dimensional (3D) structure and conformers remains a grand challenge in structural biology, primarily owing to the scarcity of experimental data, the intrinsic flexibility of RNA molecules, and the limitations of current experimental and computational methods. Here we propose trRosettaRNA2, a deep learning-based end-to-end approach to this problem. Considering the scarcity of RNA 3D structure data, trRosettaRNA2 integrates an auxiliary secondary structure (SS) prior module, pre-trained on extensive SS data, to generate informative base-pairing priors. This module also serves as an independent RNA SS prediction method, trRNA2-SS, and achieves state-of-the-art performance. To enable end-to-end prediction, trRosettaRNA2 uses SS-aware attention to generate RNA 3D structure and conformers (distinct 3D spatial arrangements of the same molecule resulting from its intrinsic flexibility). Rigorous benchmarks demonstrate that trRosettaRNA2 outperforms other RNA 3D structure prediction methods, despite using substantially fewer parameters and computational resources. Notably, its flexibility in leveraging diverse secondary structure inputs provides a pathway to generate accurate 3D structure and explore the RNA conformers. Based on trRosettaRNA2, our group, Yang-Server, was the top automated server for RNA structure prediction in the CASP16 blind test, surpassing AlphaFold 3. This performance highlights that trRosettaRNA2 represents a solid step forward for RNA structure prediction. Application to the ribonuclease P RNA demonstrates that trRosettaRNA2 successfully captures its structural heterogeneity even without requiring experimental data, showing its potential to predict RNA conformational ensembles.