Table 1.

Comparison of Methods' Performance for Single and Multiple Mutations

Training/Test Set	Model	All Mutations			Decreasing		Increasing
		R	RMSE	Slope	R	RMSE	R	RMSE
Single mutations
Skempi + Reverse/S1748	MutaBind2ˆ	0.63	1.25	0.83	0.45	1.17	0.77	1.52
Skempi/S1748	MutaBind	0.38∗	1.51	0.72	0.44	1.11	–	2.43
	BeAtMuSiC	0.30∗	1.58	0.55	0.43	1.14	−0.25∗	2.57
Test: S1748	FoldX	0.42∗	1.57	0.52	0.41	1.37	0.26∗	2.12
Test: S4191	MutaBind2 CV4	0.76	1.34	1.11	0.61	1.31	0.67	1.39
	MutaBind2 CV5	0.69	1.50	1.18	0.54	1.41	0.47	1.65
Multiple mutations
Test: M1707	MutaBind2 CV4	0.74	2.13	1.09	0.51	2.04	0.60	2.26
	MutaBind2 CV5	0.71	2.24	1.00	0.47	2.18	0.56	2.33
Test: M1337	FoldX	0.49	2.43	0.52	0.37	2.49	0.24	2.21

MutaBind2ˆ: MutaBind2 was retrained on “Skempi + Reverse” set.

∗Significant difference between MutaBind2 and other methods with p value < 0.01 calculated on a test set S1748 (implemented in R package cocor).

R, Pearson correlation coefficient between experimental and predicted ΔΔG values; RMSE (kcal mol⁻¹), root-mean-square error, the standard deviation of the residuals (prediction errors); Slope, the slope of the regression line between experimental and predicted ΔΔG values. All presented values of correlation coefficients are statistically significantly different from zero (p value << 0.01). The details about datasets are shown in Table S1.