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In vitro Kinase-to-Phosphosite database (iKiP-DB) predicts kinase activity in phosphoproteomic datasets

Item Type:Article
Title:In vitro Kinase-to-Phosphosite database (iKiP-DB) predicts kinase activity in phosphoproteomic datasets
Creators Name:Mari, T. and Mösbauer, K. and Wyler, E. and Landthaler, M. and Drosten, C. and Selbach, M.
Abstract:Phosphoproteomics routinely quantifies changes in the levels of thousands of phosphorylation sites, but functional analysis of such data remains a major challenge. While databases like PhosphoSitePlus contain information about many phosphorylation sites, the vast majority of known sites is not assigned to any protein kinase. Assigning changes in the phosphoproteome to the activity of individual kinases therefore remains a key challenge. A recent large-scale study systematically identified in vitro substrates for most human protein kinases. Here, we reprocessed and filtered these data to generate an in vitro Kinase-to-Phosphosite database (iKiP-DB). We show that iKiP-DB can accurately predict changes in kinase activity in published phosphoproteomic data sets for both well-studied and poorly characterized kinases. We apply iKiP-DB to a newly generated phosphoproteomic analysis of SARS-CoV-2 infected human lung epithelial cells and provide evidence for coronavirus-induced changes in host cell kinase activity. In summary, we show that iKiP-DB is widely applicable to facilitate the functional analysis of phosphoproteomic data sets.
Keywords:Phosphoproteomics, Kinase Enrichment, Phosphosite Annotations, Phosphosites Database, Mass Spectrometry, Tandem Mass Tags, SARS-CoV-2
Source:Journal of Proteome Research
Publisher:American Chemical Society
Page Range:1575-1587
Date:3 June 2022
Official Publication:https://doi.org/10.1021/acs.jproteome.2c00198
PubMed:View item in PubMed

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