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Comprehensive evaluation of phosphoproteomic-based kinase activity inference

Author

Listed:
  • Sophia Müller-Dott

    (Institute for Computational Biomedicine)

  • Eric J. Jaehnig

    (Baylor College of Medicine)

  • Khoi Pham Munchic

    (The Broad Institute of MIT and Harvard)

  • Wen Jiang

    (Baylor College of Medicine)

  • Tomer M. Yaron-Barir

    (Weill Cornell Medicine
    Weill Cornell Medicine
    Columbia University Vagelos College of Physicians and Surgeons)

  • Sara R. Savage

    (Baylor College of Medicine)

  • Martin Garrido-Rodriguez

    (Institute for Computational Biomedicine
    European Molecular Biology Laboratory)

  • Jared L. Johnson

    (Harvard Medical School
    Harvard Medical School)

  • Alessandro Lussana

    (European Bioinformatics Institute (EMBL-EBI))

  • Evangelia Petsalaki

    (European Bioinformatics Institute (EMBL-EBI))

  • Jonathan T. Lei

    (Baylor College of Medicine)

  • Aurelien Dugourd

    (Institute for Computational Biomedicine)

  • Karsten Krug

    (The Broad Institute of MIT and Harvard)

  • Lewis C. Cantley

    (Harvard Medical School
    Harvard Medical School)

  • D. R. Mani

    (The Broad Institute of MIT and Harvard)

  • Bing Zhang

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Julio Saez-Rodriguez

    (Institute for Computational Biomedicine
    European Bioinformatics Institute (EMBL-EBI))

Abstract

Kinases regulate cellular processes and are essential for understanding cellular function and disease. To investigate the regulatory state of a kinase, numerous methods have been developed to infer kinase activities from phosphoproteomics data using kinase-substrate libraries. However, few phosphorylation sites can be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. Moreover, inferred activities vary across methods, necessitating evaluation for accurate interpretation. Here, we present benchmarKIN, an R package enabling comprehensive evaluation of kinase activity inference methods. Alongside classical perturbation experiments, benchmarKIN introduces a tumor-based benchmarking approach utilizing multi-omics data to identify highly active or inactive kinases. We used benchmarKIN to evaluate kinase-substrate libraries, inference algorithms and the potential of adding predicted kinase-substrate interactions to overcome the coverage limitations. Our evaluation shows most computational methods perform similarly, but the choice of library impacts the inferred activities with a combination of manually curated libraries demonstrating superior performance in recapitulating kinase activities. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance. We then demonstrate how kinase activity inference aids characterize kinase inhibitor responses in cell lines. Overall, benchmarKIN helps researchers to select reliable methods for identifying deregulated kinases.

Suggested Citation

  • Sophia Müller-Dott & Eric J. Jaehnig & Khoi Pham Munchic & Wen Jiang & Tomer M. Yaron-Barir & Sara R. Savage & Martin Garrido-Rodriguez & Jared L. Johnson & Alessandro Lussana & Evangelia Petsalaki & , 2025. "Comprehensive evaluation of phosphoproteomic-based kinase activity inference," Nature Communications, Nature, vol. 16(1), pages 1-21, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59779-y
    DOI: 10.1038/s41467-025-59779-y
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    1. Fumika Koyano & Kei Okatsu & Hidetaka Kosako & Yasushi Tamura & Etsu Go & Mayumi Kimura & Yoko Kimura & Hikaru Tsuchiya & Hidehito Yoshihara & Takatsugu Hirokawa & Toshiya Endo & Edward A. Fon & Jean-, 2014. "Ubiquitin is phosphorylated by PINK1 to activate parkin," Nature, Nature, vol. 510(7503), pages 162-166, June.
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