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Graph neural network and diffusion model for modeling RNA interatomic interactions.

Marek Justyna1, Craig Zirbel2, Maciej Antczak1,3

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This study introduces GraphaRNA, a novel computational method using graph neural networks to accurately predict novel ribonucleic acid (RNA) structures. GraphaRNA generalizes well to unseen RNA families and respects user-defined constraints.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Bioinformatics

Background:

  • Ribonucleic acid (RNA) function is dictated by its 3D structure, but experimental methods like X-ray crystallography, NMR, and Cryo-EM often lack atomic resolution.
  • Current deep learning RNA structure prediction tools (e.g., AlphaFold3, trRosettaRNA) excel for known RNA families but struggle with novel or synthetic structures.
  • Accurate in silico RNA structure prediction is crucial for understanding RNA function and designing new RNA-based therapeutics.

Purpose of the Study:

  • To develop and evaluate a novel computational approach for accurate ribonucleic acid (RNA) structure prediction.
  • To explore the utility of graph neural networks (GNNs) and denoising diffusion probabilistic models (DDPMs) for learning interatomic interactions in RNA.
  • To assess the generalization capability of the proposed method on unseen RNA structures and its ability to incorporate user-defined constraints.

Main Methods:

  • RNA molecules were modeled as graphs using a coarse-grained, five-atom representation.
  • Graph neural networks and denoising diffusion probabilistic models were employed to learn interatomic interactions.
  • The model was trained and evaluated on local RNA descriptors, assessing generalization across different RNA families (rRNA, tRNA vs. others).

Main Results:

  • The proposed method demonstrated reliable prediction of structures for unseen local RNA descriptors.
  • The approach effectively adhered to user-defined constraints, including Watson-Crick-Franklin base pairing interactions.
  • GraphaRNA shows promise for predicting structures of novel RNA families beyond those in the training set.

Conclusions:

  • GraphaRNA, utilizing GNNs and DDPMs, offers a robust approach for in silico RNA structure prediction, particularly for novel or synthetic RNA sequences.
  • The method's ability to generalize and incorporate constraints enhances its utility for diverse RNA structure prediction tasks.
  • This work contributes a valuable tool for advancing structural biology and RNA-based research.