Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were! validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA in vitro and 13 PKA phosphorylation sites were identified by mass spectrometry. NetPhosK was 100% sensitive and 41% specific in predicting PKA sites in the four proteins. These results demonstrate the potential of using integrated computational and experimental methods for detailed investigations of the phosphoproteome.
|Journal||Journal of Proteome Research|
|Publication status||Published - 2004|
Hjerrild, M., Stensballe, A., Rasmussen, T. E., Kofoed, C. B., Blom, N., Sicheritz-Pontén, T., Larsen, M. R., Brunak, S., Jensen, O. N., & Gammeltoft, S. (2004). Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry. Journal of Proteome Research, 3, 426-433.