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. 2017 Jan 13;22(1):123. doi: 10.3390/molecules22010123

Table 2.

The predicted results of peptides by pharmacophore and docking.

NO. a Peptide Sequence Fitvalue b -CDOCKER ENERGY c NO. a Peptide Sequence Fitvalue b -CDOCKER ENERGY c
1-1 QEKQKL none none 2-4 GGAAGGAF 0.95 165.39
1-2 EKHNRL none none 3-1 GAAGGAF 0.96 163.62
1-3 QSGDQQEF 0.97 none 3-2 AAGGAF 0.95 143.18
1-4 VGQLGGAAGGAF 0.96 180.40 3-3 AGGAF 0.96 132.88
1-5 QQQQQQQQQQQQSL none none 3-4 GGAF 0.96 119.30
1-6 PATAHKQQQQADANMAKL none none 3-5 GAF 0.95 112.38
2-1 VGQL none 108.65 3-6 AF 0.23 99.54
2-2 GGAAGGA none 137.75 4 lisinopril 0.95 71.22
2-3 VGQ none 112.56

a No. 1-1 to 1-6 represented the sequence of six identified peptides; No. 2-1 to 2-4 represented the sequence of four peptides by in silico proteolysis of VGQLGGAAGGAF; No. 3-1 to 3-6 stood for the sequence of six peptides by sequential division from GGAAGGAF; b Fitvalues were the scores of pharmacophore screening; c -CDOCKER ENERGY was the scoring function of docking modelling and represented the interaction ability between ligands and receptor.