|
|||||||||||||||
 |
|
||||||||||||||
|
|||||||||||||||
| |||||||||||||||
|
Article abstractBACKGROUNDNetPhos is a neural network-based method for predicting
potential phosphorylation sites at serine, threonine or tyrosine residues
in protein sequences. The first version of NetPhos (v.1.0) was developed
in 1997-1998 and was only available for in-house use and research
collaborations. Version 2.0 was trained on a larger data set of known
phosphorylation sites and has been made publicly available using the WWW
or via e-mail.
REFERENCESequence- and structure-based prediction of eukaryotic protein phosphorylation sites.Blom, N., Gammeltoft, S., and Brunak, S. Journal of Molecular Biology: 294(5): 1351-1362, 1999.
ABSTRACTProtein phosphorylation at serine, threonine or tyrosine residues affects a multitude of cellular signaling processes. How is specificity in substrate recognition and phosphorylation by protein kinases achieved ? Here we present an artificial neural network method which predicts phosphorylation sites in independent sequences with a sensitivity in the range from 69% to 96%. As an example, we predict novel phosphorylation sites in the p300/CBP protein that may regulate interaction with transcription factors and histone acetyltransferase activity. In addition, serine and threonine residues in p300/CBP that can be modified by O-linked glycosylation with N-acetylglucosamine are identified. Glycosylation may prevent phosphorylation at these sites, a mechanism named yin-yang regulation.
FEEDBACK, COMMENTS AND SUGGESTIONSWe would appreciate any confirmation or the opposite of our predictions. Since an expanded data set with additional phosphorylated sequences would increase the performance of the network, we are very interested in receiving such material. User feedback is the only way we will learn to enhance the performance of the method. Any other comments regarding the predictions or the data may be sent to Nikolaj Blom at nikob@cbs.dtu.dkCORRESPONDENCE
Nikolaj Blom,
|
| Follow CBS on Twitter |
