Step 1. Paste a single sequence or several sequences in FASTA format into the field:

Step 2. Select the phosphorylated residues serine, threonine, and tyrosine which you want to predict:

Step 3. Select the protein kinase that catalyzes the phosphorylation. By default, "unknown" is selected when you do not know which kinase catalyze the phosphorylation:

Step 4. Key in the parameters of E-value and score in HMM search. Then click on the "submit" to predict:

Step 5. Instead of the prediction result, user can see the training sequences and sequence logo.

1. Protein Kinase C

Protein kinase C transduces the cellular signals that promote lipid hydrolysis. This 80kDa enzyme is recruited to the plasma membrane by diacylglycerol and, in many cases, by calcium. The enzyme is activated by diacylglycerol and phospholipid (usually PS) and is thought to undergo a conformational change upon binding to the membrane. PKC phosphorylates a variety of target proteins which control growth and cellular differentiation.

The structure of PKC is not known, but the isozymes of PKC are homologous with cAMP-dependent protein kinase (protein kinase A), and Orr and Newton have modeled the catalytic domain of the PKC beta-II isozyme, based on the structure of PKA (J. Biol. Chem. 269, 8383 (1994)).

2. cAMP-dependent Protein Kinase

We select the HMMER bit score as the criteria to define a HMM match. A search of a model with the HMMER bit score greater than the threshold t is defined as a positive prediction, i.e., a HMM recognizes a phosphorylation site. The threshold t of each model is decided by maximizing the accuracy measure during a variety of cross-validations with the HMM bit score value range from 0 to -10. For instance, the figure below depicts the optimization of the threshold of the HMM bit scores in the S_PKA model. The threshold of the S_PKA model is set to -4.5 to maximize the accuracy measure of the model.

By default, the thresholds of whole models are shown as table below.