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Related Experiment Video

Updated: Nov 1, 2025

Oligopeptide Competition Assay for Phosphorylation Site Determination

Oligopeptide Competition Assay for Phosphorylation Site Determination

Published on: May 18, 2017

Using Multilayer Heterogeneous Networks to Infer Functions of Phosphorylated Sites.

Joanne Watson^1,2, Jean-Marc Schwartz¹, Chiara Francavilla²

¹Division of Evolution & Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine & Health, University of Manchester, Manchester M13 9PT, U.K.

Journal of Proteome Research

|June 24, 2021

Summary

This summary is machine-generated.

This study introduces a novel network-based method to identify specific protein functions based on phosphorylation sites. It improves phosphoproteomics data analysis by linking phosphorylation patterns to context-specific biological roles.

Keywords:

Gene Ontology bioinformatics functional analysis multilayer networks pathways phosphoproteomics random walk

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Last Updated: Nov 1, 2025

Oligopeptide Competition Assay for Phosphorylation Site Determination

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Published on: May 18, 2017

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Identification of Kinase-substrate Pairs Using High Throughput Screening

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

Area of Science:

Proteomics
Systems Biology
Bioinformatics

Background:

Quantitative phosphoproteomics is crucial for studying cellular signaling pathways.
Current analysis methods lack site-specific functional annotations for phosphorylated proteins.
Existing gene-centric annotations do not capture phosphorylation-dependent protein functions.

Purpose of the Study:

To develop a method for associating specific phosphorylation sites with their context-dependent functions.
To refine phosphoproteomics data analysis beyond generic gene annotations.
To improve the understanding of cellular signaling through site-specific functional prediction.

Main Methods:

Integration of shotgun phosphoproteomics data, protein-protein interactions, and functional annotations into a heterogeneous multilayer network.
Application of a random walk on the heterogeneous network (RWHN) algorithm to associate phosphorylation sites with potential functions.
Validation using a MAPK/ERK pathway model and existing phosphoproteomics datasets.

Main Results:

Successfully associated differentially regulated phosphorylation sites with specific, known functions.
Reproduced experimental conclusions from three previously published phosphoproteomics studies.
Demonstrated the ability to predict context-specific functions for phosphorylation sites based on their regulatory patterns.

Conclusions:

The proposed network-based method refines phosphoproteomics data analysis.
It accurately predicts context-specific functions for phosphorylation sites.
This approach enhances the interpretation of signaling pathways by providing site-specific functional insights.