Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks
Fig 4
ThermoNet predicted well the ΔΔGs of mutations in the p53 tumor suppressor protein and myoglobin.
(A) Performance of ThermoNet on predicting ΔΔG for the direct mutations in p53 (r = 0.45, σ = 2.01 kcal/mol). (B) Performance of ThermoNet on predicting ΔΔG for the reverse mutations in p53 (r = 0.56, σ = 1.92 kcal/mol). (C) Direct versus reverse ΔΔG values of all p53 mutations predicted by ThermoNet (rdir−rev = −0.93 and 〈δ〉 = −0.04 kcal/mol). (D) Performance of ThermoNet on predicting ΔΔG for the direct mutations in myoglobin (r = 0.38, σ = 1.16 kcal/mol). (E) Performance of ThermoNet on predicting ΔΔG for the reverse mutations in myoglobin (r = 0.37, σ = 1.18 kcal/mol). (F) Direct versus reverse ΔΔG values of all myoglobin mutations predicted by ThermoNet, with a Pearson correlation of rdir−rev = −0.97 and 〈δ〉 = −0.02 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.