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Identification of Kinase-substrate Pairs Using High Throughput Screening
Published on: August 29, 2015
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Benchmarking EGF signaling pathway inference using phosphoproteomics and kinase-substrate interactions.
Martin Garrido-Rodriguez1,2,3,4, Clement Potel1, Mira Lea Burtscher1
1Molecular Systems Biology unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
Nature Communications
|February 25, 2026
Summary
This study revisits classic signaling pathways using phosphoproteomics and kinase-substrate data. We discovered many unexplored interactions, highlighting limitations in current pathway views and suggesting new research hypotheses.
Area of Science:
- Molecular Biology
- Systems Biology
- Bioinformatics
Background:
- Classic biochemistry methods limit signaling pathway interpretation.
- Advancements in phosphoproteomics and kinase-substrate interaction data offer new approaches.
- Revisiting signaling pathways is crucial for understanding complex biological processes.
Purpose of the Study:
- To infer context-specific signaling pathways using phosphoproteomics and kinase-substrate networks.
- To comprehensively characterize the epidermal growth factor (EGF) response.
- To identify unexplored interactions within signaling pathways.
Main Methods:
- Meta-analysis of EGF response data.
- Generation of comprehensive EGF response datasets.
- Inference and comparison of kinase-kinase pathways against ground truth sets.
- Evaluation of network propagation methods.
Main Results:
- Literature-curated networks showed the highest recovery of ground-truth interactions.
- Network propagation methods provided modest gains in interaction recovery.
- Up to 90% of inferred interactions were not present in existing ground truth sets.
- Significant unexplored interactions are supported by available data.
Conclusions:
- Traditional views on signaling pathways are limited.
- Phosphoproteomics and network analysis reveal numerous unexplored interactions.
- This study provides opportunities for generating novel mechanistic hypotheses in cell signaling.
