biostudies-literatureBogebo RNovo Nordisk Foundation Center for Protein Researche94672https://www.ebi.ac.uk/biostudies/studies/S-EPMC3983226biostudies-literatureUnknown9(4)Recent progress in the understanding of seven-transmembrane receptor (7TMR) signalling has promoted the development of a new generation of pathway selective ligands. The angiotensin II type I receptor (AT1aR) is one of the most studied 7TMRs with respect to selective activation of the β-arrestin dependent signalling. Two complimentary global phosphoproteomics studies have analyzed the complex signalling induced by the AT1aR. Here we integrate the data sets from these studies and perform a joint analysis using a novel method for prediction of differential kinase activity from phosphoproteomics data. The method builds upon NetworKIN, which applies sophisticated linear motif analysis in combination with contextual network modelling to predict kinase-substrate associations with high accuracy aPloS onePredicting kinase activity in angiotensin receptor phosphoproteomes based on sequence-motifs and interactions.PMC3983226PI Lars Juhl JensenPI Jesper Velgaard OlsenOlsen JVHorn HGammeltoft SHansen JLBogebo RChristensen GLJensen LJfalsePredicting kinase activity in angiotensin receptor phosphoproteomes based on sequence-motifs and interactions.Recent progress in the understanding of seven-transmembrane receptor (7TMR) signalling has promoted the development of a new generation of pathway selective ligands. The angiotensin II type I receptor (AT1aR) is one of the most studied 7TMRs with respect to selective activation of the β-arrestin dependent signalling. Two complimentary global phosphoproteomics studies have analyzed the complex signalling induced by the AT1aR. Here we integrate the data sets from these studies and perform a joint analysis using a novel method for prediction of differential kinase activity from phosphoproteomics data. The method builds upon NetworKIN, which applies sophisticated linear motif analysis in combination with contextual network modelling to predict kinase-substrate associations with high accuracy a2014-01-01T00:00:00Z20142025-04-18T17:38:00.399Z2019-03-26T23:25:40ZS-EPMC39832262472269110.1371/journal.pone.0094672