title: IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data.
abstract: BACKGROUND
  Phosphoproteomics measurements are widely applied in cellular biology to detect changes in signalling dynamics. However, due to the inherent complexity of phosphorylation patterns and the lack of knowledge on how phosphorylations are related to functions, it is often not possible to directly deduce protein activities from those measurements. Here, we present a heuristic machine learning algorithm that infers the activities of kinases from Phosphoproteomics data using kinase-target information from the PhosphoSitePlus database. By comparing the estimated kinase activity profiles to the measured phosphosite profiles, it is furthermore possible to derive the kinases that are most likely to phosphorylate the respective phosphosite.
  
  
  RESULTS
  We apply our approach to published datasets of the human cell cycle generated from HeLaS3 cells, and insulin signalling dynamics in mouse hepatocytes. In the first case, we estimate the activities of 118 at six cell cycle stages and derive 94 new kinase-phosphosite links that can be validated through either database or motif information. In the second case, the activities of 143 kinases at eight time points are estimated and 49 new kinase-target links are derived.
  
  
  METHODS
  The algorithm is implemented in Matlab and be downloaded from github. It makes use of the Optimization and Statistics toolboxes. https://github.com/marcel-mischnik/IKAP.git.
  
  
  BACKGROUND
  marcel.mischnik@gmail.com
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.
authors.forename: Marcel
authors.initials: M
authors.lastname: Mischnik
authors.affiliation: Department of Proteomics and Signal Transduction, Max-Planck-Institute for Biochemistry, Martinsried, Germany.
authors.forename: Francesca
authors.initials: F
authors.lastname: Sacco
authors.affiliation: Department of Proteomics and Signal Transduction, Max-Planck-Institute for Biochemistry, Martinsried, Germany.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Cox
authors.affiliation: Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.
authors.forename: Hans-Christoph
authors.initials: HC
authors.lastname: Schneider
authors.affiliation: Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.
authors.forename: Matthias
authors.initials: M
authors.lastname: Schäfer
authors.affiliation: Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.
authors.forename: Manfred
authors.initials: M
authors.lastname: Hendlich
authors.affiliation: Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.
authors.forename: Daniel
authors.initials: D
authors.lastname: Crowther
authors.affiliation: Department of Proteomics and Signal Transduction, Max-Planck-Institute for Biochemistry, Martinsried, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Mann
authors.affiliation: Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany and.
authors.forename: Thomas
authors.initials: T
authors.lastname: Klabunde
mesh_terms: D000465:Algorithms; D000818:Animals; D002453:Cell Cycle / Q000502:physiology; D018797:Cell Cycle Proteins / Q000378:metabolism; D002478:Cells, Cultured; D016208:Databases, Factual; D006367:HeLa Cells; D022781:Hepatocytes / Q000166:cytology / Q000378:metabolism*; D000066506:Heuristics; D006801:Humans; D007328:Insulin / Q000378:metabolism; D051379:Mice; D010750:Phosphoproteins / Q000378:metabolism*; D010766:Phosphorylation; D011494:Protein Kinases / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D012984:Software
chemical_list: D018797:Cell Cycle Proteins; D007328:Insulin; D010750:Phosphoproteins; D011494:Protein Kinases