Journal article
Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry
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.
| Language: | English |
|---|---|
| Year: | 2004 |
| Pages: | 426-433 |
| ISSN: | 15353907 and 15353893 |
| Types: | Journal article |
| DOI: | 10.1021/pr0341033 |
| ORCIDs: | Blom, Nikolaj |
Algorithms Animals Artificial Intelligence COS Cells Cell Cycle Proteins Chlorocebus aethiops Cloning, Molecular Computer Simulation Cyclic AMP-Dependent Protein Kinases DNA-Binding Proteins Homeodomain Proteins Humans Immunoprecipitation Mice Nerve Tissue Proteins Nuclear Proteins Phosphorylation Protein-Tyrosine Kinases RFX5 protein, human Regulatory Factor X Transcription Factors Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization WEE1 protein, human Wee1 protein, mouse engrailed 2 protein necdin
