title: Prediction of 492 human protein kinase substrate specificities.
abstract: BACKGROUND
  Complex intracellular signaling networks monitor diverse environmental inputs to evoke appropriate and coordinated effector responses. Defective signal transduction underlies many pathologies, including cancer, diabetes, autoimmunity and about 400 other human diseases. Therefore, there is high impetus to define the composition and architecture of cellular communications networks in humans. The major components of intracellular signaling networks are protein kinases and protein phosphatases, which catalyze the reversible phosphorylation of proteins. Here, we have focused on identification of kinase-substrate interactions through prediction of the phosphorylation site specificity from knowledge of the primary amino acid sequence of the catalytic domain of each kinase.
  
  
  RESULTS
  The presented method predicts 488 different kinase catalytic domain substrate specificity matrices in 478 typical and 4 atypical human kinases that rely on both positive and negative determinants for scoring individual phosphosites for their suitability as kinase substrates. This represents a marked advancement over existing methods such as those used in NetPhorest (179 kinases in 76 groups) and NetworKIN (123 kinases), which consider only positive determinants for kinase substrate prediction. Comparison of our predicted matrices with experimentally-derived matrices from about 9,000 known kinase-phosphosite substrate pairs revealed a high degree of concordance with the established preferences of about 150 well studied protein kinases. Furthermore for many of the better known kinases, the predicted optimal phosphosite sequences were more accurate than the consensus phosphosite sequences inferred by simple alignment of the phosphosites of known kinase substrates.
  
  
  CONCLUSIONS
  Application of this improved kinase substrate prediction algorithm to the primary structures of over 23, 000 proteins encoded by the human genome has permitted the identification of about 650, 000 putative phosphosites, which are posted on the open source PhosphoNET website (http://www.phosphonet.ca).
authors.affiliation: Department of Computer Science, University of British Columbia, Vancouver, Canada. jsafaei@cs.ubc.ca.
authors.forename: Javad
authors.initials: J
authors.lastname: Safaei
authors.forename: Ján
authors.initials: J
authors.lastname: Maňuch
authors.forename: Arvind
authors.initials: A
authors.lastname: Gupta
authors.forename: Ladislav
authors.initials: L
authors.lastname: Stacho
authors.forename: Steven
authors.initials: S
authors.lastname: Pelech