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Table 4 Performance of Prethermut and other computational methods on the S-dataset

From: Predicting changes in protein thermostability brought about by single- or multi-site mutations

Method

r

Q2 (%)

na

CC/PBSA

0.56

78.6

478

EGAD

0.59

71.0

1065

FoldX

0.5

69.5

1200

Hunter

0.45

69.4

1594

I-Mutant2.0

0.54

77.5

933

Rosetta

0.26

73.4

1913

Combining method

0.64

80.8

407

Prethermut (RF)b

0.72

78.6

2156

Prethermut (SVM)c

0.70

83.2

2156

  1. See Methods for definitions of overall accuracy (Q2) and Pearson correlation coefficient (r). The prediction results of CC/PBSA, EGAD, FoldX, Hunter, I-Mutant 2.0, Rosetta, and Combining method were obtained from Potapov et al. [2]. an is the number of mutant proteins for which the method correctly predicted the change in thermostability. bThe number of trees in the Random forests (RF) method is 10000. The results were obtained by a 10-fold cross validation on the S-dataset. cThe parameters for the support vector machine (SVM) method are gamma (g) = 2, cost (c) = 4, and the weight for the positive samples (w) = 5. The results were obtained by a 10-fold cross validation on the S-dataset.