Predicting kinase substrates using conservation of local motif density.
BACKGROUND
Protein kinases represent critical links in cell signaling. A central problem in computational biology is to systematically identify their substrates.


RESULTS
This study introduces a new method to predict kinase substrates by extracting evolutionary information from multiple sequence alignments in a manner that is tolerant to degenerate motif positioning. Given a known consensus, the new method (ConDens) compares the observed density of matches to a null model of evolution and does not require labeled training data. We confirmed that ConDens has improved performance compared with several existing methods in the field. Further, we show that it is generalizable and can predict interesting substrates for several important eukaryotic kinases where training data is not available.


METHODS
ConDens can be found at http://www.moseslab.csb.utoronto.ca/andyl/.


BACKGROUND
alan.moses@utoronto.ca


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Cell and Systems Biology, University of Toronto, Toronto, Canada M5S 3G5.
Andy C W
AC
Lai
Alex N
AN
Nguyen Ba
Alan M
AM
Moses
D000595:Amino Acid Sequence; D017489:CDC28 Protein Kinase, S cerevisiae / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence; D015233:Models, Statistical; D010766:Phosphorylation; D010770:Phosphotransferases / Q000737:chemistry*; D054730:Protein Interaction Domains and Motifs; D016415:Sequence Alignment; D013379:Substrate Specificity
D010770:Phosphotransferases; D017489:CDC28 Protein Kinase, S cerevisiae