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Table 6 Distribution of amino acid types in data set

From: Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Mutated amino acid In data set Hot spots (ΔΔG≥ 2 kcal/mol) Enrichment in hot spots
  (Number) (%) (Number) (%) (a) (%) (b)  
Arg 33 9.46 7 21.21 8.64 0.91
Asn 22 6.30 6 27.27 7.41 1.18
Asp 29 8.31 9 31.03 11.11 1.34
Cys 1 0.29 0 0.00 0.00 0.00
Gln 21 6.02 2 9.52 2.47 0.41
Glu 31 8.88 5 16.13 6.17 0.69
His 13 3.72 1 7.69 1.23 0.33
Ile 15 4.30 4 26.67 4.94 1.15
Leu 10 2.87 1 10.00 1.23 0.43
Lys 32 9.17 11 34.38 13.58 1.48
Met 2 0.57 0 0.00 0.00 0.00
Phe 11 3.15 2 18.18 2.47 0.78
Ser 28 8.02 1 3.57 1.23 0.15
Thr 24 6.88 1 4.17 1.23 0.18
Trp 23 6.59 9 39.13 11.11 1.69
Tyr 44 12.61 20 45.45 24.69 1.96
Val 10 2.87 2 20.00 2.47 0.86
  1. The number and percentage of amino acids in our data set are shown. The number and percentage of hot spots for each amino acid type is also reported. For a given amino acid type, (%)(a)is the percentage of hot spots with respect to the residues of that type in the data set (i.e. entry in column 4 divided by the corresponding entry in column 2); (%)(b)is the percentage of hot spots of that type with respect to all hot spots in the data set (i.e. entry in column 4 divided by the sum of all entries in column 4). Enrichment in hot spots is calculated as the ratio of the frequency of a given residue type in hot spots (column 6) over the frequency of the same amino acid type in the whole data set (column 3). This table should be compared with Table 2 in [19]. Note that proline and glycine are not included in our data set.