Identification of Phosphorylation Sites in Protein Kinase A Substrates Using Artificial Neural Networks and Mass Spectrometry

@article{citeulike:2675935, abstract = {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. Keywords: protein kinase A phosphorylation site prediction neural network analysis mass spectrometry}, address = {Department of Clinical Biochemistry, Glostrup Hospital, Nordre Ringvej 57, DK-2600 Glostrup, Denmark, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark, Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Anker Engelunds Vej 1, DK-2800 Kgs. Lyngby, Denmark, and Department of Medicinal Chemistry, Division of Pharmacognosy, Uppsala University, BMC, 751 23 Uppsala, Sweden}, author = {Hjerrild, M. and Stensballe, A. and Rasmussen, T. E. and Kofoed, C. B. and Blom, N. and Sicheritz-Ponten, T. and Larsen, M. R. and Brunak, S. and Jensen, O. N. and Gammeltoft, S. }, citeulike-article-id = {2675935}, doi = {10.1021/pr0341033}, journal = {J. Proteome Res.}, keywords = {article-predikin, bioinformatics, mass-spec, netphosk, neural-network, phosphorylation, prediction}, month = {June}, number = {3}, pages = {426--433}, posted-at = {2008-04-16 04:05:30}, priority = {2}, title = {Identification of Phosphorylation Sites in Protein Kinase A Substrates Using Artificial Neural Networks and Mass Spectrometry}, url = {http://dx.doi.org/10.1021/pr0341033}, volume = {3}, year = {2004} }

TY - JOUR ID - citeulike:2675935 L3 - citeulike-article-id:2675935 TI - Identification of Phosphorylation Sites in Protein Kinase A Substrates Using Artificial Neural Networks and Mass Spectrometry JF - J. Proteome Res. VL - 3 IS - 3 SP - 426 EP - 433 AD - Department of Clinical Biochemistry, Glostrup Hospital, Nordre Ringvej 57, DK-2600 Glostrup, Denmark, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark, Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Anker Engelunds Vej 1, DK-2800 Kgs. Lyngby, Denmark, and Department of Medicinal Chemistry, Division of Pharmacognosy, Uppsala University, BMC, 751 23 Uppsala, Sweden N2 - 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. Keywords: protein kinase A phosphorylation site prediction neural network analysis mass spectrometry KW - article-predikin KW - bioinformatics KW - mass-spec KW - netphosk KW - neural-network KW - phosphorylation KW - prediction AU - Hjerrild, M AU - Stensballe, A AU - Rasmussen, TE AU - Kofoed, CB AU - Blom, N AU - Sicheritz-Ponten, T AU - Larsen, MR AU - Brunak, S AU - Jensen, ON AU - Gammeltoft, S PY - 2004/06/14/ UR - http://dx.doi.org/10.1021/pr0341033 ER -