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SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation.

Yashas B L Samaga1, Shampa Raghunathan1, U Deva Priyakumar1

  • 1Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India.

The Journal of Physical Chemistry. B
|September 21, 2021
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Summary
This summary is machine-generated.

This study introduces SCONES, a novel machine learning method for predicting protein stability changes from mutations. SCONES leverages transitive consistency for improved accuracy and reduced data dependence in protein engineering.

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Area of Science:

  • Protein engineering and computational biology.
  • Application of machine learning in predicting protein properties.

Background:

  • Protein engineering relies on mutating amino acids to achieve desired functions, necessitating stable proteins.
  • Experimental methods for assessing protein stability are time-consuming, limiting large-scale sequence space exploration.
  • Existing machine learning methods predict thermodynamic stability changes but have limitations in consistency evaluation.

Purpose of the Study:

  • To develop a novel machine learning method that incorporates both symmetric and transitive properties for predicting protein stability changes.
  • To introduce a new dataset, S^transitive, for evaluating transitive consistency in stability predictions.
  • To create an interpretable neural network for predicting relative stability changes of missense mutations.

Main Methods:

  • Developed SCONES, an interpretable neural network architecture incorporating symmetric and transitive consistency.
  • Utilized transitive data augmentation and the S^transitive dataset for model evaluation.
  • Estimated residue contributions to protein stability (ΔG) within local structural environments.

Main Results:

  • SCONES demonstrates immunity to common dataset biases and reduced reliance on data compared to existing methods.
  • The model shows robustness to overfitting and explains a significant portion of experimental data variance.
  • SCONES accurately predicts small relative protein stability changes (ΔΔG) for missense mutations without significant structural alteration.

Conclusions:

  • SCONES offers a self-consistent and interpretable approach to predicting protein stability changes.
  • The method enhances the efficiency and accuracy of protein engineering by providing reliable stability predictions.
  • This work advances the field of computational protein design through novel machine learning techniques and data augmentation strategies.