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Phospho-Proteomics

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Cover of 'Phospho-Proteomics'

Table of Contents

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    Book Overview
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    Chapter 1 Thiol-ene-Enabled Detection of Thiophosphorylation as a Labeling Strategy for Phosphoproteins.
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    Chapter 2 Phosphopeptide Detection with Biotin-Labeled Phos-tag.
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    Chapter 3 Phosphopeptide Enrichment by Covalent Chromatography After Solid Phase Derivatization of Protein Digests on Reversed Phase Supports.
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    Chapter 4 Peptide Labeling Using Isobaric Tagging Reagents for Quantitative Phosphoproteomics.
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    Chapter 5 Identification of Direct Kinase Substrates Using Analogue-Sensitive Alleles.
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    Chapter 6 Quantitative Analysis of Tissue Samples by Combining iTRAQ Isobaric Labeling with Selected/Multiple Reaction Monitoring (SRM/MRM).
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    Chapter 7 Enrichment Strategies in Phosphoproteomics.
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    Chapter 8 Phosphopeptide Enrichment by Immobilized Metal Affinity Chromatography.
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    Chapter 9 The Use of Titanium Dioxide for Selective Enrichment of Phosphorylated Peptides.
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    Chapter 10 Sequential Elution from IMAC (SIMAC): An Efficient Method for Enrichment and Separation of Mono- and Multi-phosphorylated Peptides.
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    Chapter 11 Improving the Phosphoproteome Coverage for Limited Sample Amounts Using TiO2-SIMAC-HILIC (TiSH) Phosphopeptide Enrichment and Fractionation.
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    Chapter 12 Offline High pH Reversed-Phase Peptide Fractionation for Deep Phosphoproteome Coverage.
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    Chapter 13 Phosphopeptide Enrichment Using Various Magnetic Nanocomposites: An Overview.
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    Chapter 14 Two Dimensional Gel Electrophoresis-Based Plant Phosphoproteomics.
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    Chapter 15 Variable Digestion Strategies for Phosphoproteomics Analysis.
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    Chapter 16 Online LC-FAIMS-MS/MS for the Analysis of Phosphorylation in Proteins.
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    Chapter 17 Simple and Reproducible Sample Preparation for Single-Shot Phosphoproteomics with High Sensitivity.
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    Chapter 18 Identification of Direct Kinase Substrates via Kinase Assay-Linked Phosphoproteomics.
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    Chapter 19 Phosphoprotein Detection by High-Throughput Flow Cytometry.
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    Chapter 20 Resources for Assignment of Phosphorylation Sites on Peptides and Proteins.
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    Chapter 21 From Phosphosites to Kinases.
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    Chapter 22 Phospho-Proteomics
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    Chapter 23 Systems Analysis for Interpretation of Phosphoproteomics Data.
Attention for Chapter 21: From Phosphosites to Kinases.
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Chapter title
From Phosphosites to Kinases.
Chapter number 21
Book title
Phospho-Proteomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3049-4_21
Pubmed ID
Book ISBNs
978-1-4939-3048-7, 978-1-4939-3049-4
Authors

Stephanie Munk, Jan C. Refsgaard, Jesper V. Olsen, Lars J. Jensen

Editors

Louise von Stechow

Abstract

Kinases play a pivotal role in propagating the phosphorylation-mediated signaling networks in living cells. With the overwhelming quantities of phosphoproteomics data being generated, the number of identified phosphorylation sites (phosphosites) is ever increasing. Often, proteomics investigations aim to understand the global signaling modulation that takes place in different biological conditions investigated. For phosphoproteomics data, identifying the kinases central to mediating this response is key. This has prompted several efforts to catalogue the immense amounts of phosphorylation data and known or predicted kinases responsible for the modifications. However, barely 20 % of the known phosphosites are assigned to a kinase, initiating various bioinformatics efforts that attempt to predict the responsible kinases. These algorithms employ different approaches to predict kinase consensus sequence motifs, mostly based on large scale in vivo and in vitro experiments. The context of the kinase and the phosphorylated proteins in a biological system is equally important for predicting association between the enzymes and substrates, an aspect that is also being tackled with available bioinformatics tools. This chapter summarizes the use of the larger phosphorylation databases, and approaches that can be applied to predict kinases that phosphorylate individual sites or that are globally modulated in phosphoproteomics datasets.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 27%
Student > Bachelor 5 14%
Student > Master 4 11%
Researcher 4 11%
Professor 1 3%
Other 2 5%
Unknown 11 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 32%
Agricultural and Biological Sciences 6 16%
Computer Science 4 11%
Sports and Recreations 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 12 32%