DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks

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.