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Ribonucleic acid (RNA) is a complex macromolecule essential for living organisms to function in cells. Understanding its three-dimensional (3D) structure is critical for elucidating its cellular roles. However, computational prediction of RNA 3D structures remains a significant challenge due to the vast conformational space that RNA molecules can adopt. Although machine learning, particularly deep learning-based methods, has recently gained traction, the lack of a large dataset of native RNA structures for training has limited these methods from achieving desired performance. In this study, we leverage pre-trained RNA large language models to predict RNA 3D conformations directly from input RNA sequences. Specifically, we introduce data augmentation techniques to address the issue of data scarcity in RNA 3D structures. This present paper focuses on predicting backbone conformations to evaluate the effectiveness of our method. Preliminary results demonstrate promising accuracy, with predicted structures achieving an average RMSD of 3.85Å against native 3D structures in the PDB—a 50% reduction in performance error compared to predictions made without the data augmentation method.

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Leveraging RNA LLMs for 3D Structure Prediction via Data Augmentation

  • 会议论文

  • 发表日期：

    2026年

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  • Sixiang Zhang,
  • Harish Anand,
  • Liming Cai

摘要

Ribonucleic acid (RNA) is a complex macromolecule essential for living organisms to function in cells. Understanding its three-dimensional (3D) structure is critical for elucidating its cellular roles. However, computational prediction of RNA 3D structures remains a significant challenge due to the vast conformational space that RNA molecules can adopt. Although machine learning, particularly deep learning-based methods, has recently gained traction, the lack of a large dataset of native RNA structures for training has limited these methods from achieving desired performance. In this study, we leverage pre-trained RNA large language models to predict RNA 3D conformations directly from input RNA sequences. Specifically, we introduce data augmentation techniques to address the issue of data scarcity in RNA 3D structures. This present paper focuses on predicting backbone conformations to evaluate the effectiveness of our method. Preliminary results demonstrate promising accuracy, with predicted structures achieving an average RMSD of 3.85Å against native 3D structures in the PDB—a 50% reduction in performance error compared to predictions made without the data augmentation method.