Comprehensive evaluation of phosphoproteomic-based kinase activity inference

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.

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Publication Details

Journal
Nature Communications
Published
2025-05-22
DOI
https://doi.org/10.1038/s41467-025-59779-y
Citations
7
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
5.64

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article

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

Julio Sáez-Rodríguez, Eric J. Jaehnig, Karsten Krug, D.R. Mani et al.
7 citations
Nature Communications
Bioinformatics and Genomic Networks
5.64
article

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

Julio Sáez-Rodríguez, Eric J. Jaehnig, Karsten Krug, D.R. Mani, Aurélien Dugourd, Sara R. Savage, Jonathan T. Lei, Martín Garrido‐Rodríguez, Jared L. Johnson, Lewis C. Cantley, Alessandro Lussana, Tomer M. Yaron, Evangelia Petsalaki, Sophia Müller‐Dott, Wen Jiang, Khoi Pham Munchic, Bing Zhang
article en
7 citations

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.

Nature CommunicationsVol. 16(1)
Broad Institute (US), European Bioinformatics Institute (GB), Harvard University (US), Baylor College of Medicine (US), Cornell University (US), Heidelberg University (DE), University Hospital Heidelberg (DE), Dana-Farber Cancer Institute (US), European Molecular Biology Laboratory (DE), Baylor Genetics (US), Weill Cornell Medicine (US), Columbia University (US)
Robert and Janice McNair Foundation, Cancer Prevention and Research Institute of Texas, European Molecular Biology Laboratory, Bundesministerium für Bildung und Forschung, National Institutes of Health, National Cancer Institute
Good health and well-being
Openalex Percentile: Top 4%
Bioinformatics and Genomic Networks
5.64
DOIOpen Access PDFOpenAlex
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