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Related Experiment Video

Updated: Nov 16, 2025

Identification of Kinase-substrate Pairs Using High Throughput Screening

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

Robust inference of kinase activity using functional networks.

Serhan Yılmaz¹, Marzieh Ayati², Daniela Schlatzer³

¹Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA. serhan.yilmaz@case.edu.

Nature Communications

|February 20, 2021

Summary

This summary is machine-generated.

We developed RoKAI, a network-based tool that improves kinase activity inference from phosphoproteomic data. This method enhances disease-related kinase identification for targeted therapies.

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Area of Science:

Biochemistry
Systems Biology
Bioinformatics

Background:

Mass spectrometry is key for high-throughput phosphoproteomic analysis.
Phosphoproteomic data aids in inferring kinase activity, crucial for understanding diseases like cancer and neurodegenerative disorders.
Current methods for kinase activity inference can be limited by data completeness and integration of functional information.

Purpose of the Study:

To present RoKAI, a network-based framework to enhance kinase activity inference.
To integrate diverse functional information for a more robust analysis of signaling pathways.
To improve the identification of dysregulated kinases in disease contexts.

Main Methods:

Developed RoKAI, a network-based computational framework.
Integrated multiple sources of functional information to capture coordinated signaling changes.
Utilized computational experiments to validate the framework's performance.

Main Results:

Phosphorylation sites within a kinase's functional neighborhood significantly predict its activity.
RoKAI consistently improves the accuracy and robustness of kinase activity inference methods.
The framework is effective even with missing data annotations and quantifications.

Conclusions:

RoKAI enhances the reliability of kinase activity inference from phosphoproteomic data.
This approach facilitates the identification of understudied kinases implicated in diseases.
RoKAI holds potential for developing novel kinase inhibitors for targeted therapeutic strategies.