NetPhos 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.
Sequence- and structure-based prediction of eukaryotic protein
Blom, N., Gammeltoft, S., and Brunak, S.
Journal of Molecular Biology: 294(5): 1351-1362, 1999.
Protein 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 SUGGESTIONS
We 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