Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry
| Autor: | Ole N. Jensen, Thomas E. Rasmussen, Allan Stensballe, Martin R. Larsen, Steen Gammeltoft, Christine B Kofoed, Majbrit Hjerrild, Nikolaj Blom, Thomas Sicheritz-Pontén, Søren Brunak |
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| Předmět: |
inorganic chemicals
Immunoprecipitation Cell Cycle Proteins Nerve Tissue Proteins Regulatory Factor X Transcription Factors Biology Biochemistry DNA-binding protein Mice Artificial Intelligence Chlorocebus aethiops Animals Humans Computer Simulation Protein phosphorylation Cloning Molecular Phosphorylation Nuclear protein Protein kinase A Cell Cycle Protein Homeodomain Proteins COS cells Nuclear Proteins General Chemistry Protein-Tyrosine Kinases Cyclic AMP-Dependent Protein Kinases DNA-Binding Proteins Spectrometry Mass Matrix-Assisted Laser Desorption-Ionization COS Cells Algorithms |
| Zdroj: | Technical University of Denmark Orbit |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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