Table 3.
The comparison among PredPhospho, PPSP, GPS 2.0, KiasePhos 2.0, and our method.
| Tools | PredPhospho | GPS 2.0 | PPSP | KinasePhos 2.0 | Our method |
|---|---|---|---|---|---|
| Method | SVM | GPS | BDT | SVM | SVM |
| Training feature | Sequence | Sequence | Sequence | Sequence | Sequence + 3D structural information |
| Material | PhosphoBase + Swiss-Prot | Phospho.ELM | Phospho.ELM | Phospho.ELM + UniProtKB | Phospho.ELM + UniProtKB |
| No. of kinase groups | 4 | > 100 | 68 | 58 | > 100 |
| Data input | Sequence | Sequence | Sequence | Sequence | Sequence, PDB ID or structure |
| 3D structure visualization | - | - | - | - | JMol |
| PKA group | Sn = 70.1% Sp = 86.4% |
Sn = 88.2% Sp = 86.6% |
Sn = 86.9% Sp = 83.1% |
Sn = 86.9% Sp = 85.6% |
Sn = 89.4% Sp = 87.7% |
| PKC group | Sn = 70.9% Sp = 86.5% |
Sn = 86.2% Sp = 83.0% |
Sn = 82.9% Sp = 85.5% |
Sn = 0.84 Sp = 0.86 |
Sn = 84.3% Sp = 89.1% |
| CK2 group | Sn = 82.0% Sp = 92.8% |
Sn = 81.4% Sp = 86.4% |
Sn = 84.0% Sp = 90.5% |
Sn = 86.2% Sp = 86.4% |
Sn = 88.1% Sp = 90.2% |
| SRC group | - | Sn = 82.3% Sp = 86.8% |
Sn = 78.0% Sp = 74.6% |
Sn = 86.4% Sp = 82.2% |
Sn = 86.4% Sp = 86.2% |
The highlights are marked in bold. For PKA group, our method has highest sensitivity and specificity. For PKC group, GPS 2.0 has highest sensitivity and our method has highest specificity. For CK2 group, our method has highest sensitivity and PredPhospho has highest specificity. For SRC group, our method has highest sensitivity and GPS 2.0 has highest specificity.
Abbreviation: SVM, support vector machine; MCL, Markov cluster algorithm; GPS, group-based phosphorylation scoring method; BDT, Bayesian decision theory; MDD, maximal dependence decomposition; HMM, hidden Markov model; AAC, amino acid composition; CP, coupling pattern; SA, structural alphabet; Sn, sensitivity; Sp, specificity; Acc, accuracy.