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J Proteome Res. 2004 May-Jun;3(3):426-33.

Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry.

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1
Department of Clinical Biochemistry, Glostrup Hospital, Nordre Ringvej 57, DK-2600 Glostrup, Denmark. mh@dcb-glostrup.dk

Abstract

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.

PMID:
15253423
[Indexed for MEDLINE]
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