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G4STAB: a multi-input deep learning model to predict G-quadruplex thermodynamic stability based on sequence and salt

Donn Liew1, Akesha Dinuli Dharmatilleke1, Edwin See1

  • 1Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore.

Bioinformatics (Oxford, England)
|September 27, 2025
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Summary
This summary is machine-generated.

G4STAB, a deep learning model, predicts G-quadruplex (G4) DNA stability using sequence, salt, and pH, improving accuracy without predefined structures. Cancer-like conditions significantly alter G4 stability, revealing genomic patterns.

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Area of Science:

  • Genomics
  • Biophysics
  • Computational Biology

Background:

  • G-quadruplexes (G4s) are crucial non-canonical DNA structures influencing gene regulation and stability.
  • G4 thermodynamic stability is key to their biological roles but challenging to predict accurately.
  • Existing models for G4 stability lack adaptability to diverse topologies and environmental factors like ion concentration and pH.

Purpose of the Study:

  • To develop a novel deep learning model, G4STAB, for accurate prediction of DNA G-quadruplex melting temperatures.
  • To integrate sequence features, salt concentration, and pH for enhanced G4 stability prediction.
  • To explore the impact of cellular environmental factors on G4 stability profiles.

Main Methods:

  • Developed G4STAB, a multi-input deep learning neural network.
  • Trained the model on 2382 diverse DNA G4 sequences.
  • Validated G4STAB's predictive accuracy (R²=0.8) against experimental data, focusing on sequence and environmental factors.

Main Results:

  • G4STAB accurately predicts G4 melting temperatures without relying on predetermined structural features.
  • The model identifies novel sequence-stability relationships and confirms known G4 stability determinants.
  • Analysis of 391,502 G4s shows cancer-like ionic environments significantly alter G4 stability, increasing structures with physiological melting temperatures by 13.5-fold.

Conclusions:

  • G4STAB offers a robust framework for predicting G4 stability, accounting for sequence and environmental variables.
  • Cellular ionic environments, particularly those mimicking cancer, profoundly impact G4 stability.
  • Genomic-wide analysis reveals systematic patterns in G4 stability responses across chromosomes and gene types.