ArticleMolecular & cellular proteomics : MCP2017
Kinase activity ranking using phosphoproteomics data (KARP) quantifies the contribution of protein kinases to the regulation of cell viability.
Article in Molecular & cellular proteomics : MCP, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 27 citations in OpenAlex.
- Principles of phosphoproteomics and applications in cancer research.The Biochemical journal · 2023Article
- KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data.Nature communications · 2022Article
- eEF2K Activity Determines Synergy to Cotreatment of Cancer Cells With PI3K and MEK Inhibitors.Molecular & cellular proteomics : MCP · 2022Article
- Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA).BMC cancer · 2021Article
- Mouse models of growth hormone deficiency.Reviews in endocrine & metabolic disorders · 2021Review
- Robust inference of kinase activity using functional networks.Nature communications · 2021Article
- Article
- Proteomics and phosphoproteomics in precision medicine: applications and challenges.Briefings in bioinformatics · 2019Review
- INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases.Molecular systems biology · 2019Article
- The temporal profile of activity-dependent presynaptic phospho-signalling reveals long-lasting patterns of poststimulus regulation.PLoS biology · 2019Article
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4 authors at 1 institution in 1 country.
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Abstract
Cell survival is regulated by a signaling network driven by the activity of protein kinases; however, determining the contribution that each kinase in the network makes to such regulation remains challenging. Here, we report a computational approach that uses mass spectrometry-based phosphoproteomics data to rank protein kinases based on their contribution to cell regulation. We found that the scores returned by this algorithm, which we have termed kinase activity ranking using phosphoproteomics data (KARP), were a quantitative measure of the contribution that individual kinases make to the signaling output. Application of KARP to the analysis of eight hematological cell lines revealed that cyclin-dependent kinase (CDK) 1/2, casein kinase (CK) 2, extracellular signal-related kinase (ERK), and p21-activated kinase (PAK) were the most frequently highly ranked kinases in these cell models. The patterns of kinase activation were cell-line specific yet showed a significant association with cell viability as a function of kinase inhibitor treatment. Thus, our study exemplifies KARP as an untargeted approach to empirically and systematically identify regulatory kinases within signaling networks.
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