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RNA Secondary Structure Prediction Using High-throughput SHAPE
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R3J-AGNN: GNN-Based Prediction of Inter-Branch Angles in RNA Three-Way Junctions from Secondary Structure.

Hu Yang1, Ning Qiao2, Bengong Zhang1

  • 1Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, China.

Biology
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Summary

This study introduces R3J-AGNN, a novel graph neural network for predicting RNA three-way junction geometry. The method accurately models complex RNA structures from secondary information, aiding 3D scaffold reconstruction.

Keywords:
RNA structure predictionRNA three-way junctiongraph neural networkinter-branch angles

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Accurate RNA 3D structure prediction is crucial but challenging for multi-branch motifs.
  • The global topology of RNA three-way junctions (3WJs) is sensitive to helical stem orientations.
  • Existing methods struggle with precise modeling of complex RNA junctions.

Purpose of the Study:

  • To develop a computational model for predicting inter-branch angles of RNA 3WJs.
  • To enable accurate 3D scaffold reconstruction of 3WJ-containing RNA structures.
  • To provide geometric constraints for RNA tertiary structure modeling and refinement.

Main Methods:

  • Proposed R3J-AGNN, a dual-resolution hierarchical graph neural network.
  • Integrated nucleotide-level interactions with coarse-grained topology representation.
  • Predicted inter-branch angles directly from RNA secondary structure information.

Main Results:

  • R3J-AGNN demonstrated robust and consistent predictive performance on an independent test set.
  • The model accurately infers inter-branch geometry for RNA 3WJs.
  • Enabled subsequent reconstruction of 3D RNA scaffolds.

Conclusions:

  • R3J-AGNN effectively predicts RNA three-way junction geometry from secondary structures.
  • The model offers valuable geometric constraints for RNA tertiary structure modeling.
  • Advances RNA 3D structure prediction capabilities for complex motifs.