Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks
Fig 5
Predicted ΔΔG distributions of ClinVar missense variants.
(A) The overall ΔΔG distributions of ClinVar variants predicted by ThermoNet and FoldX. ThermoNet’s predictions are consistent with the expected range based on experimentally determined ΔΔG values (-5 kcal/mol to +5 kcal/mol). In contrast, more than 15% of ΔΔGs predicted by FoldX are outside the expected range. (B) The ΔΔG distributions for ClinVar benign variants predicted by ThermoNet and FoldX. (C) The ΔΔG distributions of ClinVar pathogenic variants predicted by ThermoNet and FoldX. The ΔΔGs of 80.2% of benign variants predicted by ThermoNet fall within the neutral zone (-0.5 to +0.5 kcal/mol, region between dashed lines), in which variants are not expected to influence fitness. FoldX only predicted 39.7% of benign variants to be in the neutral zone. Further, the ΔΔGs of pathogenic variants predicted by ThermoNet suggest pathogenic variants are nearly equally likely to be stabilizing (47.3%) as destabilizing (52.7%). In contrast, FoldX predicted that 83.2% of pathogenic variants are destabilizing. Variants for which FoldX ΔΔG is > 20 kcal/mol are omitted for clarity. Percentages represent the fractions of variants whose ΔΔGs are predicted to be in the neutral zone.