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KinasePhos is to computationally predict phosphorylation sites within given protein sequences. The known phosphorylation sites are categorized by substrate sequences and their corresponding protein kinase classes. Profile Hidden Markov Model (HMM) is applied for learning to each group of sequences surrounding to the phosphorylation residues. By comparing to other approaches previously developed, our method has higher accuracy and provides not only the location of the phosphorylation sites, but also the corresponding catalytic protein kinases.

Case Study 1 CP1BB_HUMAN, Platelet glycoprotein Ib beta chain [Precursor]
Case Study 2 BAD_MOUSE, Bcl2-antagonist of cell death
Case Study 3 ANX2_HUMAN, Annexin A2


Submission

Paste a single sequence or several sequences in FASTA format into the field below:

Submit a file (< 2MB) in FASTA format directly from your local disk:

Predict on: Serine(S) Threonine(T) Tyrosine(Y)

Kinase :

with Prediction Specificity

by default HMM bit score

with HMM bit score >

 

 

Bid Lab, Institute of Bioinformatics, National Chiao Tung University , Taiwan.
Contact us:bryan@mail.nctu.edu.tw with questions or comments.
Websites:http://KinasePhos.mbc.nctu.edu.tw/