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A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors

A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors

Published on: April 29, 2022

From Phosphosites to Kinases.

Stephanie Munk¹, Jan C Refsgaard^1,2, Jesper V Olsen¹

¹Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark.

Methods in Molecular Biology (Clifton, N.J.)

|November 21, 2015

Summary

This summary is machine-generated.

Identifying kinases is crucial for understanding cell signaling. Bioinformatics tools help predict kinases for numerous phosphorylation sites, aiding phosphoproteomics data analysis.

Keywords:

Kinases NetPhorest NetworKIN PHOSIDA PhoshoSitePlus Phospho.ELM Phosphoproteomics

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Last Updated: Mar 30, 2026

A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors

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Published on: April 29, 2022

Identification of Kinase-substrate Pairs Using High Throughput Screening

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Published on: August 29, 2015

Oligopeptide Competition Assay for Phosphorylation Site Determination

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Published on: May 18, 2017

Area of Science:

Cellular Biology
Biochemistry
Bioinformatics

Background:

Kinases mediate critical phosphorylation-dependent signaling networks in cells.
Phosphoproteomics generates vast data, increasing identified phosphorylation sites (phosphosites).
Understanding kinase roles in cellular responses is key for interpreting phosphoproteomics data.

Purpose of the Study:

To review phosphorylation databases and bioinformatics approaches for kinase prediction.
To address the challenge of assigning kinases to identified phosphosites.
To explore methods for predicting kinases involved in global signaling modulation.

Main Methods:

Utilizing large-scale phosphorylation databases.
Employing bioinformatics algorithms for kinase prediction based on sequence motifs.
Considering biological context for enzyme-substrate association prediction.

Main Results:

Less than 20% of known phosphosites are currently assigned to a specific kinase.
Bioinformatics tools offer diverse strategies for kinase prediction.
Contextual information is increasingly incorporated into kinase-substrate prediction models.

Conclusions:

Effective kinase identification is essential for advancing phosphoproteomics research.
Bioinformatics plays a vital role in predicting kinase-substrate relationships.
Further development of predictive tools is needed to fully annotate phosphoproteomics datasets.