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Updated: Nov 4, 2025

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

KEA3: improved kinase enrichment analysis via data integration.

Maxim V Kuleshov¹, Zhuorui Xie¹, Alexandra B K London¹

¹Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.

Nucleic Acids Research

|May 21, 2021

Summary

Kinase Enrichment Analysis 3 (KEA3) predicts upstream kinases from phosphoproteomics data. Integrating kinase-substrate and kinase-protein interactions improves kinase prediction accuracy for cellular signaling studies.

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Area of Science:

* Molecular Biology
* Bioinformatics
* Systems Biology

Background:

* Phosphoproteomics and proteomics offer broad views of cellular signaling but do not directly indicate kinase activity.
* Identifying upstream kinases is crucial for understanding signaling pathway regulation.
* Existing tools may not fully leverage diverse interaction data for kinase prediction.

Purpose of the Study:

* To introduce Kinase Enrichment Analysis 3 (KEA3), a webserver for predicting upstream kinases.
* To enhance the analysis of phosphoproteomics and proteomics data by inferring kinase activity.
* To improve the prediction accuracy of kinases responsible for observed phosphorylation events.

Main Methods:

* KEA3 utilizes a background database of kinase-substrate interactions (KSI) and kinase-protein interactions (KPI).
* The webserver infers overrepresentation of kinases upstream of user-provided protein lists.
* Performance was benchmarked using data from single-kinase perturbations and small-molecule kinase inhibitors.

Main Results:

* KEA3 successfully predicts perturbed kinases from experimental data.
* Integrating KSIs and KPIs from multiple sources enhances kinase recovery.
* A composite ranking strategy improves the accuracy of upstream kinase identification.

Conclusions:

* KEA3 provides a valuable tool for analyzing large-scale phosphoproteomics and proteomics data.
* Integrating diverse interaction data sources is key to robust kinase prediction.
* The KEA3 webserver facilitates the study of cellular signaling networks and kinase regulation.