title: Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry.
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.
authors.affiliation: Department of Clinical Biochemistry, Glostrup Hospital, Nordre Ringvej 57, DK-2600 Glostrup, Denmark. mh@dcb-glostrup.dk
authors.forename: Majbrit
authors.initials: M
authors.lastname: Hjerrild
authors.forename: Allan
authors.initials: A
authors.lastname: Stensballe
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Rasmussen
authors.forename: Christine B
authors.initials: CB
authors.lastname: Kofoed
authors.forename: Nikolaj
authors.initials: N
authors.lastname: Blom
authors.forename: Thomas
authors.initials: T
authors.lastname: Sicheritz-Ponten
authors.forename: Martin R
authors.initials: MR
authors.lastname: Larsen
authors.forename: Søren
authors.initials: S
authors.lastname: Brunak
authors.forename: Ole N
authors.initials: ON
authors.lastname: Jensen
authors.forename: Steen
authors.initials: S
authors.lastname: Gammeltoft
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D019556:COS Cells; D018797:Cell Cycle Proteins / Q000378:metabolism; D002522:Chlorocebus aethiops; D003001:Cloning, Molecular; D003198:Computer Simulation; D017868:Cyclic AMP-Dependent Protein Kinases / Q000378:metabolism*; D004268:DNA-Binding Proteins / Q000378:metabolism*; D018398:Homeodomain Proteins / Q000378:metabolism; D006801:Humans; D047468:Immunoprecipitation; D051379:Mice; D009419:Nerve Tissue Proteins / Q000378:metabolism*; D009687:Nuclear Proteins / Q000378:metabolism*; D010766:Phosphorylation; D011505:Protein-Tyrosine Kinases / Q000378:metabolism; D000071701:Regulatory Factor X Transcription Factors; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D000099266:Engrailed 2 Protein
chemical_list: D018797:Cell Cycle Proteins; D017868:Cyclic AMP-Dependent Protein Kinases; D004268:DNA-Binding Proteins; D018398:Homeodomain Proteins; D009419:Nerve Tissue Proteins; D009687:Nuclear Proteins; D011505:Protein-Tyrosine Kinases; D000071701:Regulatory Factor X Transcription Factors; D000099266:Engrailed 2 Protein; C093524:RFX5 protein, human; C069766:necdin; C499349:WEE1 protein, human; C499348:Wee1 protein, mouse