Accurately predicting changes in protein stability due to mutations is important for protein engineering and for understanding the functional consequences of missense mutations in proteins. We have developed DeepDDG, a neural network-based method, for use in the prediction of changes in the stability of proteins due to point mutations. The neural network was trained on more than 5700 manually curated experimental data points and was able to obtain a Pearson correlation coefficient of 0.48-0.56 for three independent test sets, which outperformed 11 other methods. Detailed analysis of the input features shows that the solvent accessible surface area of the mutated residue is the most important feature, which suggests that the buried hydrophobic area is the major determinant of protein stability. We expect this method to be useful for large-scale design and engineering of protein stability. The neural network is freely available to academic users at http://protein.org.cn/ddg.html.
ASJC Scopus subject areas
- Chemical Engineering(all)
- Computer Science Applications
- Library and Information Sciences