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Benchmarking substrate-based kinase activity inference using phosphoproteomic data.

Claudia Hernandez-Armenta1, David Ochoa1, Emanuel Gonçalves1

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.

Bioinformatics (Oxford, England)
|February 16, 2017
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Summary
This summary is machine-generated.

Benchmarking kinase activity inference strategies using phosphoproteomics data revealed that Z-test and gene set enrichment analysis (GSEA) performed best. Kinase substrate information and evidence type significantly impact prediction accuracy.

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Area of Science:

  • * Bioinformatics
  • * Systems Biology
  • * Computational Biology

Background:

  • * Phosphoproteomic experiments are crucial for studying cellular signaling dynamics.
  • * Inferring kinase activity from phosphorylation changes is a proposed method, but lacks benchmarking.
  • * Current approaches require robust validation strategies.

Purpose of the Study:

  • * To benchmark and compare various kinase activity inference strategies.
  • * To evaluate the impact of weighting methods and substrate evidence on prediction accuracy.
  • * To establish reliable methods for analyzing phosphoproteomic data.

Main Methods:

  • * Utilized curated phosphoproteomic experiments and a gold standard dataset (184 kinase-condition pairs).
  • * Compared Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment analysis (GSEA), and regression models.
  • * Tested weighted variants (Z-test, GSEA) incorporating kinase sequence specificity and analyzed substrate number/evidence type.

Main Results:

  • * Z-test and GSEA demonstrated the best performance, with a mean Area Under the ROC Curve (AUC) of 0.722.
  • * Weighting kinase targets by sequence preference yielded marginal improvements.
  • * The number of known kinase substrates and the type of supporting evidence (in vivo, in vitro, in silico) strongly influenced prediction outcomes.

Conclusions:

  • * Z-test and GSEA are effective strategies for kinase activity inference from phosphoproteomic data.
  • * Kinase substrate information and evidence quality are critical factors for accurate predictions.
  • * This study provides a benchmark for evaluating and improving kinase activity inference methods.