Bayesian mapping of protein kinases to vasopressin-regulated phosphorylation sites in renal collecting duct.

Vasopressin controls water permeability in the renal collecting duct by regulating the water channel protein, aquaporin-2 (AQP2). Phosphoproteomic studies have identified multiple proteins that undergo phosphorylation changes in response to vasopressin. The kinases responsible for the phosphorylation of most of these sites have not been identified. Here, we use large-scale Bayesian data integration to predict the responsible kinases for 51 phosphoproteomically identified vasopressin-regulated phosphorylation sites in the renal collecting duct. To do this, we applied Bayes' rule to rank the 515 known mammalian protein kinases for each site. Bayes' rule was applied recursively to integrate each of the seven independent datasets, each time using the posterior probability vector of a given step as the prior probability vector of the next step. In total, 30 of the 33 phosphorylation sites that increase with vasopressin were predicted to be phosphorylated by protein kinase A (PKA) catalytic subunit-α, consistent with prior studies implicating PKA in vasopressin signaling. Eighteen of the vasopressin-regulated phosphorylation sites were decreased in response to vasopressin and all but three of these sites were predicted to be targets of extracellular signal-regulated kinases, ERK1 and ERK2. This result implies that ERK1 and ERK2 are inhibited in response to vasopressin V2 receptor occupation, secondary to PKA activation. The six phosphorylation sites not predicted to be phosphorylated by PKA or ERK1/2 are potential targets of other protein kinases previously implicated in aquaporin-2 regulation, including cyclin-dependent kinase 18 (CDK18), calmodulin-dependent kinase 2δ (CAMK2D), AMP-activated kinase catalytic subunit-α-1 (PRKAA1) and CDC42 binding protein kinase ß (CDC42BPB).NEW & NOTEWORTHY Vasopressin regulates water transport in the renal collecting duct in part through phosphorylation or dephosphorylation of proteins that regulate aquaporin-2. Prior studies have identified 51 vasopressin-regulated phosphorylation sites in 45 proteins. This study uses Bayesian data integration techniques to combine information from multiple prior proteomics and transcriptomics studies to predict the protein kinases that phosphorylate the 51 sites. Most of the regulated sites were predicted to be phosphorylated by protein kinase A or ERK1/ERK2.

A resource database for protein kinase substrate sequence-preference motifs based on large-scale mass spectrometry data.

BACKGROUND: Protein phosphorylation is one of the most prevalent posttranslational modifications involved in molecular control of cellular processes, and is mediated by over 520 protein kinases in humans and other mammals. Identification of the protein kinases responsible for phosphorylation events is key to understanding signaling pathways. Unbiased phosphoproteomics experiments have generated a wealth of data that can be used to identify protein kinase targets and their preferred substrate sequences. METHODS: This study utilized prior data from mass spectrometry-based studies identifying sites of protein phosphorylation after in vitro incubation of protein mixtures with recombinant protein kinases. PTM-Logo software was used with these data to generate position-dependent Shannon information matrices and sequence motif 'logos'. Webpages were constructed for facile access to logos for each kinase and a new stand-alone application was written in Python that uses the position-dependent Shannon information matrices to identify kinases most likely to phosphorylate a particular phosphorylation site. RESULTS: A database of kinase substrate target preference logos allows browsing, searching, or downloading target motif data for each protein kinase ( https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/ ). These logos were combined with phylogenetic analysis of protein kinase catalytic sequences to reveal substrate preference patterns specific to particular groups of kinases ( https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinaseTree.html ). A stand-alone program, KinasePredictor, is provided ( https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinasePredictor.html ). It takes as input, amino-acid sequences surrounding a given phosphorylation site and generates a ranked list of protein kinases most likely to phosphorylate that site. CONCLUSIONS: This study provides three new resources for protein kinase characterization. It provides a tool for prediction of kinase-substrate interactions, which in combination with other types of data (co-localization, etc.), can predict which kinases are likely responsible for a given phosphorylation event in a given tissue. Video Abstract.