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PSP-GNM: Predicting Protein Stability Changes upon Point Mutations with a Gaussian Network Model.

Sambit Kumar Mishra1,2

  • 1Cancer Genomics Research Laboratory, Leidos Biomedical Research, Inc., Rockville, MD 20850, USA.

International Journal of Molecular Sciences
|September 23, 2022
PubMed
Summary

We developed a new method, Protein Stability Prediction with a Gaussian Network Model (PSP-GNM), to predict changes in protein stability due to missense mutations. PSP-GNM accurately calculates the Gibbs free energy change (ΔΔG) and shows strong antisymmetry for mutations.

Keywords:
Gaussian network modelsGibbs free energy changeMiyazawa–Jernigan potentialmissense mutationsprotein stability

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Missense mutations can significantly alter protein stability, impacting biological functions.
  • Accurate prediction of protein stability changes is crucial for understanding disease mechanisms and protein engineering.
  • Existing methods for predicting the effects of mutations on protein stability have limitations.

Purpose of the Study:

  • To introduce a novel computational approach, Protein Stability Prediction with a Gaussian Network Model (PSP-GNM), for evaluating the impact of single amino acid substitutions on protein stability.
  • To quantify the change in unfolding Gibbs free energy (ΔΔG) upon mutation using PSP-GNM.
  • To assess the accuracy and performance of PSP-GNM against experimental data and compare it with existing state-of-the-art methods.

Main Methods:

  • Utilized a coarse-grained Gaussian Network Model (GNM) with amino acid interactions weighted by the Miyazawa-Jernigan statistical potential.
  • Simulated partial unfolding of wild-type and mutant protein structures to calculate ΔΔG based on differences in energy and entropy.
  • Validated the method on three benchmark datasets (S350, S669, S611) and assessed antisymmetry on the Ssym+ dataset.

Main Results:

  • Achieved a Pearson correlation coefficient of 0.61 between calculated and experimental ΔΔG, comparable to state-of-the-art methods.
  • Observed improved correlation with experimental ΔΔG when focusing on data near 25 °C and neutral pH, indicating condition dependence.
  • Demonstrated near-perfect antisymmetry (Pearson correlation of -0.97) for forward and reverse mutations, a significant improvement over existing approaches.

Conclusions:

  • PSP-GNM provides a robust and accurate method for predicting the effects of missense mutations on protein stability.
  • The model's ability to capture antisymmetry is a key advantage for analyzing mutation-stability relationships.
  • The developed PSP-GNM tool is implemented in Python and available as a stand-alone code for broader accessibility.