IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data

Mischnik, Marcel; Sacco, Francesca; Cox, Jürgen; Schneider, Hans‐Christoph; Schäfer, Matthias; Hendlich, Manfred; Crowther, Daniel; Mann, Matthias; Klabunde, Thomas

doi:10.1093/bioinformatics/btv699

Cited by 72 publications

(65 citation statements)

References 17 publications

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“…This type of analysis can be performed in individual samples, but is limited by a focus on phosphorylation of the kinase itself, rather than the (usually extensive) set of its substrates. Instead, several substrate‐centric approaches, focusing on phosphopeptides derived from kinase targets, also exist, including KSEA (Casado et al , ; Terfve et al , ; Wilkes et al , ), pCHIPS (Drake et al , ), and IKAP (Mischnik et al , ). The only single‐sample implementation of substrate‐centric kinase activity analysis is KARP and has been reported recently (Wilkes et al , ).…”

Section: Introductionmentioning

confidence: 99%

INKA , an integrative data analysis pipeline for phosphoproteomic inference of active kinases

Beekhof

Alphen

Henneman

et al. 2019

Molecular Systems Biology

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Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry‐based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label‐free kinase‐centric and substrate‐centric information in an Integrative Inferred Kinase Activity ( INKA ) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase–substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild‐type versus mutant, +/− drug), (iii) pre‐ and on‐treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient‐derived tumor xenografts with INKA ‐guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate‐based single‐sample tool KARP , and underscore target potential of high‐ranking kinases, encouraging further exploration of INKA 's functional and clinical value.

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Section: Introductionmentioning

confidence: 99%

INKA , an integrative data analysis pipeline for phosphoproteomic inference of active kinases

Beekhof

Alphen

Henneman

et al. 2019

Molecular Systems Biology

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“…We next used a machine learning algorithm, IKAP (Mischnik et al, 2016), to infer kinase activity based on the substrate phosphorylation levels from phosphoproteome after normalization of phosphopeptide abundance by protein abundance (Wu et al, 2011). This analysis extracted the activities for 66 kinases, of which 34 were differential regulated and hierarchically clustered into four major clusters: patterns similar to PPC1, PPC2, PPC5 and PPC7 (Figure 3D and Data S3B).…”

Section: Resultsmentioning

confidence: 99%

“…Kinase activity was inferred based on known substrates in the PhosphoSitePlus database (Hornbeck et al, 2015) using the IKAP algorithm (Mischnik et al, 2016). TF activity was inferred based on target expression levels (Gerstein et al, 2012).…”

Section: Methodsmentioning

confidence: 99%

Integrative Proteomics and Phosphoproteomics Profiling Reveals Dynamic Signaling Networks and Bioenergetics Pathways Underlying T Cell Activation

et al. 2017

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SUMMARY The molecular circuits by which antigens activate quiescent T cells remain poorly understood. We combined temporal profiling of the whole proteome and phosphoproteome via multiplexed isobaric labeling proteomics technology, computational pipelines for integrating multi-omics datasets, and functional perturbation to systemically reconstruct regulatory networks underlying T cell activation. T cell receptors activated the T cell proteome and phosphoproteome with discrete kinetics, marked by early dynamics of phosphorylation and delayed ribosome biogenesis and mitochondrial activation. Systems biology analyses identified multiple functional modules, active kinases, transcription factors and connectivity between them, and mitochondrial pathways including mitoribosomes and complex IV. Genetic perturbation revealed physiological roles for mitochondrial enzyme COX10-mediated oxidative phosphorylation in T cell quiescence exit. Our multi-layer proteomics profiling, integrative network analysis and functional studies define landscapes of the T cell proteome and phosphoproteome and reveal signaling and bioenergetics pathways that mediate lymphocyte exit from quiescence.

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“…Firstly, our computational approach predicted the in vivo activity of TFs and K/Ps. For that purpose, we considered prior-knowledge on regulatory interactions and mathematical approaches that have been developed to predict the activity status of transcription factors [16,17] and kinases [18,19] (Fig 1A). The activity of regulatory proteins is difficult to measure directly, yet provides functional information about the protein regulators involved in a cellular response.…”

Section: Introductionmentioning

confidence: 99%

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

et al. 2017

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Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

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IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 72 publications

References 17 publications

INKA , an integrative data analysis pipeline for phosphoproteomic inference of active kinases

INKA , an integrative data analysis pipeline for phosphoproteomic inference of active kinases

Integrative Proteomics and Phosphoproteomics Profiling Reveals Dynamic Signaling Networks and Bioenergetics Pathways Underlying T Cell Activation

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

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