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Structure-aware graph learning predicts RNA editability across tissues and species.

Gal Oren1, Zohar Rosenwasser2, Michael Levitt1

  • 1Bar-Ilan University, Israel.

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Summary
This summary is machine-generated.

Predicting RNA editing by ADAR enzymes is challenging. ADAREDIT, a novel graph-attention framework, accurately predicts RNA editing sites by considering double-stranded RNA structure, outperforming existing methods.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Adenosine-to-inosine (A-to-I) RNA editing, mediated by ADAR enzymes, is a crucial post-transcriptional modification with therapeutic potential.
  • Predicting RNA editing sites is complex, as ADAR enzyme recognition relies on double-stranded RNA (dsRNA) geometry and stability, not solely on sequence.
  • Current predictive models often fall short due to their inability to fully capture the structural determinants of ADAR substrate recognition.

Purpose of the Study:

  • To develop a structure-explicit computational framework, ADAREDIT, for predicting A-to-I RNA editing sites.
  • To improve the accuracy of RNA editing site prediction by integrating dsRNA structural information with sequence data.
  • To investigate the conserved principles of ADAR substrate recognition across different species.

Main Methods:

  • Developed ADAREDIT, a graph-attention framework representing dsRNA substrates as nucleotide graphs with detailed edge features.
  • Augmented the graph representation with sequence information and typed interactions to capture complex recognition patterns.
  • Trained and evaluated ADAREDIT on a dataset of 905 inverted Alu duplexes with predicted secondary structures and extensive RNA sequencing data from 8,603 GTEx samples across 47 tissues.

Main Results:

  • ADAREDIT significantly outperformed sequence-only models (CNN, transformer, RNA language models) in predicting RNA editing across multiple tissue contexts.
  • Achieved high discrimination performance (AUROC/AUPRC = 0.96; F1 ≈ 0.90) on combined tissue data, demonstrating robust predictive power.
  • The graph representation successfully transferred to evolutionarily distant non-Alu species, indicating conserved ADAR recognition principles.
  • Attention analysis revealed known biochemical constraints and identified novel, longer-range structural influences on RNA editing.

Conclusions:

  • ADAREDIT provides a powerful, structure-aware approach for predicting A-to-I RNA editing sites, advancing the potential of RNA editing therapeutics.
  • The findings highlight the critical role of dsRNA structure in ADAR enzyme recognition and suggest conserved mechanisms across diverse species.
  • This framework offers a valuable tool for understanding RNA editing and guiding the design of programmable RNA editing strategies.