title: Machine Learning of Global Phosphoproteomic Profiles Enables Discrimination of Direct versus Indirect Kinase Substrates.
abstract: Mass spectrometry allows quantification of tens of thousands of phosphorylation sites from minute amounts of cellular material. Despite this wealth of information, our understanding of phosphorylation-based signaling is limited, in part because it is not possible to deconvolute substrate phosphorylation that is directly mediated by a particular kinaseversusphosphorylation that is mediated by downstream kinases. Here, we describe a framework for assignment of directin vivokinase substrates using a combination of selective chemical inhibition, quantitative phosphoproteomics, and machine learning techniques. Our workflow allows classification of phosphorylation events following inhibition of an analog-sensitive kinase into kinase-independent effects of the inhibitor, direct effects on cognate substrates, and indirect effects mediated by downstream kinases or phosphatases. We applied this method to identify many direct targets of Cdc28 and Snf1 kinases in the budding yeastSaccharomyces cerevisiaeGlobal phosphoproteome analysis of acute time-series demonstrated that dephosphorylation of direct kinase substrates occurs more rapidly compared with indirect substrates, both after inhibitor treatment and under a physiological nutrient shift inwtcells. Mutagenesis experiments revealed a high proportion of functionally relevant phosphorylation sites on Snf1 targets. For example, Snf1 itself was inhibited through autophosphorylation on Ser391and new phosphosites were discovered that modulate the activity of the Reg1 regulatory subunit of the Glc7 phosphatase and the Gal83 β-subunit of SNF1 complex. This methodology applies to any kinase for which a functional analog sensitive version can be constructed to facilitate the dissection of the global phosphorylation network.
authors.affiliation: From the ‡Institute for Research in Immunology and Cancer.
authors.forename: Evgeny
authors.initials: E
authors.lastname: Kanshin
authors.affiliation: From the ‡Institute for Research in Immunology and Cancer.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Giguère
authors.affiliation: From the ‡Institute for Research in Immunology and Cancer.
authors.forename: Cheng
authors.initials: C
authors.lastname: Jing
authors.affiliation: From the ‡Institute for Research in Immunology and Cancer, pierre.thibault@umontreal.ca md.tyers@umontreal.ca.|§Department of Medicine.
authors.forename: Mike
authors.initials: M
authors.lastname: Tyers
authors.affiliation: From the ‡Institute for Research in Immunology and Cancer, pierre.thibault@umontreal.ca md.tyers@umontreal.ca.|¶Department of Chemistry, Université de Montréal, C.P. 6128, Succursale centre-ville, Montréal, Québec, H3C 3J7, Canada.
authors.forename: Pierre
authors.initials: P
authors.lastname: Thibault
mesh_terms: D000465:Algorithms; D004252:DNA Mutational Analysis; D000069550:Machine Learning; D010750:Phosphoproteins / Q000378:metabolism*; D010766:Phosphorylation / Q000187:drug effects; D011494:Protein Kinases / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D011720:Pyrazoles / Q000494:pharmacology; D011743:Pyrimidines / Q000494:pharmacology; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D013379:Substrate Specificity / Q000187:drug effects
chemical_list: C479693:1-tert-butyl-3-naphthalen-1-ylmethyl-1H-pyrazolo(3,4-d)pyrimidin-4-ylemine; D010750:Phosphoproteins; D011720:Pyrazoles; D011743:Pyrimidines; D029701:Saccharomyces cerevisiae Proteins; D011494:Protein Kinases