Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks
Fig 3
Performance of ThermoNet on the blind test set.
(A) Performance of ThermoNet on predicting ΔΔG for direct mutations; The Pearson correlation coefficient (r) between predicted values and experimentally determined values is 0.47, and the root-mean-square error (σ) of predicted values from experimentally determined values is 1.56 kcal/mol. The dots are colored in gradient from blue to red such that blue represents the most accurate prediction and red indicates the least accurate prediction. (B) Cumulative distribution of ThermoNet prediction error on direct mutations. (C) Performance of ThermoNet on predicting ΔΔG for the reverse mutations (r = 0.47, σ = 1.55 kcal/mol). (D) Cumulative distribution of ThermoNet prediction error on reverse mutations. (E) Direct versus reverse ΔΔG values of all the mutations in the blind test set predicted by ThermoNet. A perfectly unbiased predictor would give r = −1 and 〈δ〉 = 0 kcal/mol. ThermoNet successfully reduces prediction bias with r = −0.96 and 〈δ〉 = −0.01 kcal/mol. (F) Distribution of ThermoNet prediction bias.