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RNA-TorsionBERT: leveraging language models for RNA 3D torsion angles prediction.

Clément Bernard1,2, Guillaume Postic1, Sahar Ghannay2

  • 1Université Paris Saclay, Univ Evry, IBISC, Evry-Courcouronnes 91020, France.

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|January 8, 2025
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Summary
This summary is machine-generated.

This study introduces RNA-TorsionBERT, a novel language model for predicting RNA torsional angles directly from sequence data. This approach enhances RNA 3D structure prediction accuracy and quality evaluation.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Genomics

Background:

  • Predicting RNA 3D structure remains a significant challenge.
  • RNA 3D structures depend on residue distances, base interactions, and backbone torsional angles.
  • Accurate prediction of torsional angles is crucial for global RNA folding reconstruction.

Purpose of the Study:

  • To develop a novel method for directly predicting RNA torsional angles from raw sequence data.
  • To adapt language models for the specific task of RNA structure prediction.
  • To improve the accuracy of RNA 3D structure prediction through enhanced torsional angle prediction.

Main Methods:

  • Developed RNA-TorsionBERT, a language-based model utilizing sequential interactions.
  • Applied the model to predict RNA torsional and pseudo-torsional angles solely from sequence.
  • Inferred a torsion angle-dependent scoring function (TB-MCQ) using model predictions.

Main Results:

  • RNA-TorsionBERT demonstrates improved prediction of torsional angles compared to state-of-the-art methods.
  • The TB-MCQ scoring function accurately evaluates the quality of near-native predicted RNA structures based on torsion angles.
  • The model shows promising results for advancing RNA 3D structure prediction.

Conclusions:

  • Language models offer significant potential for advancing RNA 3D structure prediction.
  • Direct prediction of torsional angles from sequence is a viable approach.
  • The developed methods and tools contribute to improved RNA structural analysis.