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DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks
Huali Cao1, Jingxue Wang1, Liping He1
1Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering , East China Normal University , Shanghai 200062 , China.
Journal of Chemical Information and Modeling
|February 15, 2019
Summary
Predicting protein stability changes from mutations is crucial. DeepDDG, a new neural network method, accurately forecasts these changes, outperforming existing tools and highlighting the importance of solvent accessible surface area.
Area of Science:
- Biochemistry
- Computational Biology
- Protein Engineering
Background:
- Accurate prediction of protein stability changes due to mutations is vital for protein engineering.
- Understanding missense mutations' functional impact is critical in biological research.
Purpose of the Study:
- To develop and validate DeepDDG, a novel neural network-based method for predicting protein stability changes caused by point mutations.
Main Methods:
- A neural network model was trained using over 5700 curated experimental data points.
- The method's performance was evaluated on three independent test sets using Pearson correlation coefficients.
- Input features were analyzed to identify key determinants of protein stability.
Main Results:
- DeepDDG achieved a Pearson correlation coefficient of 0.48-0.56 across independent test sets.
- The developed method outperformed 11 other existing prediction tools.
- Solvent accessible surface area of the mutated residue emerged as the most significant predictive feature.
Conclusions:
- DeepDDG demonstrates high accuracy in predicting protein stability changes due to mutations.
- The findings emphasize the role of buried hydrophobic areas in determining protein stability.
- DeepDDG is a valuable tool for large-scale protein stability engineering and design.
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