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Updated: Jun 26, 2026

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

KEA: kinase enrichment analysis.

Alexander Lachmann¹, Avi Ma'ayan

¹Department of Pharmacology and Systems Therapeutics, Systems Biology Center in New York, Icahn Medical Institute, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.

Bioinformatics (Oxford, England)

|January 30, 2009

Summary

This summary is machine-generated.

This study introduces Kinase Enrichment Analysis (KEA), a tool that links protein/gene lists to kinases. KEA helps hypothesize kinase roles in cellular states and phenotype modulation.

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Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

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Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Published on: June 30, 2019

Area of Science:

Cellular and Molecular Biology
Systems Biology
Bioinformatics

Background:

Multivariate experiments yield lists of proteins/genes altered by treatments.
These lists can be analyzed using kinase-substrate interaction data.
Understanding kinase involvement is key to cellular state regulation and phenotype modulation.

Purpose of the Study:

To develop a method for inferring kinases associated with specific protein/gene lists.
To rank kinases and kinase families based on their likelihood of regulating cellular conditions.
To aid in generating hypotheses about kinome function in cellular states.

Main Methods:

Projecting lists of altered proteins/genes onto known kinase-substrate interactions.
Computing the deviation of kinase proportions from expected distributions.
Utilizing a web-based tool with an integrated kinase-substrate database.

Main Results:

Identification of kinases and kinase families functionally associated with experimental conditions.
Ranking of kinases based on enrichment probability.
Facilitation of hypothesis generation for cellular regulation.

Conclusions:

Kinase Enrichment Analysis (KEA) is a web tool linking mammalian proteins/genes to their phosphorylating kinases.
KEA computes enrichment probability by comparing kinase-substrate proportions against a background database.
The system aids in understanding kinome involvement in cellular states and phenotype modulation.