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Proteomis in Systems Biology

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Cover of 'Proteomis in Systems Biology'

Table of Contents

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    Book Overview
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    Chapter 1 Multiplexed Quantitative Proteomics for High-Throughput Comprehensive Proteome Comparisons of Human Cell Lines.
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    Chapter 2 Sample Preparation Approaches for iTRAQ Labeling and Quantitative Proteomic Analyses in Systems Biology.
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    Chapter 3 Two Birds with One Stone: Parallel Quantification of Proteome and Phosphoproteome Using iTRAQ.
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    Chapter 4 Selected Reaction Monitoring to Measure Proteins of Interest in Complex Samples: A Practical Guide.
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    Chapter 5 Monitoring PPARG-Induced Changes in Glycolysis by Selected Reaction Monitoring Mass Spectrometry.
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    Chapter 6 A Targeted MRM Approach for Tempo-Spatial Proteomics Analyses.
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    Chapter 7 Targeted Phosphoproteome Analysis Using Selected/Multiple Reaction Monitoring (SRM/MRM).
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    Chapter 8 Testing Suitability of Cell Cultures for SILAC-Experiments Using SWATH-Mass Spectrometry.
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    Chapter 9 Combining Amine-Reactive Cross-Linkers and Photo-Reactive Amino Acids for 3D-Structure Analysis of Proteins and Protein Complexes.
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    Chapter 10 Tissue MALDI Mass Spectrometry Imaging (MALDI MSI) of Peptides.
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    Chapter 11 Ethyl Esterification for MALDI-MS Analysis of Protein Glycosylation.
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    Chapter 12 Characterization of Protein N-Glycosylation by Analysis of ZIC-HILIC-Enriched Intact Proteolytic Glycopeptides.
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    Chapter 13 Simple and Effective Affinity Purification Procedures for Mass Spectrometry-Based Identification of Protein-Protein Interactions in Cell Signaling Pathways.
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    Chapter 14 A Systems Approach to Understand Antigen Presentation and the Immune Response.
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    Chapter 15 Profiling of Small Molecules by Chemical Proteomics.
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    Chapter 16 Generating Sample-Specific Databases for Mass Spectrometry-Based Proteomic Analysis by Using RNA Sequencing.
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    Chapter 17 A Proteomic Workflow Using High-Throughput De Novo Sequencing Towards Complementation of Genome Information for Improved Comparative Crop Science.
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    Chapter 18 From Phosphoproteome to Modeling of Plant Signaling Pathways.
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    Chapter 19 Interpretation of Quantitative Shotgun Proteomic Data.
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    Chapter 20 A Simple Workflow for Large Scale Shotgun Glycoproteomics.
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    Chapter 21 Systemic Analysis of Regulated Functional Networks.
Attention for Chapter 18: From Phosphoproteome to Modeling of Plant Signaling Pathways.
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Chapter title
From Phosphoproteome to Modeling of Plant Signaling Pathways.
Chapter number 18
Book title
Proteomics in Systems Biology
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3341-9_18
Pubmed ID
Book ISBNs
978-1-4939-3339-6, 978-1-4939-3341-9
Authors

Maksim Zakhartsev, Heidi Pertl-Obermeyer, Waltraud X. Schulze

Editors

Jörg Reinders

Abstract

Quantitative proteomic experiments in recent years became almost routine in many aspects of biology. Particularly the quantification of peptides and corresponding phosphorylated counterparts from a single experiment is highly important for understanding of dynamics of signaling pathways. We developed an analytical method to quantify phosphopeptides (pP) in relation to the quantity of the corresponding non-phosphorylated parent peptides (P). We used mixed-mode solid-phase extraction to purify total peptides from tryptic digest and separated them from most of the phosphorous-containing compounds (e.g., phospholipids, nucleotides) which enhances pP enrichment on TiO2 beads. Phosphoproteomic data derived with this designed method allows quantifying pP/P stoichiometry, and qualifying experimental data for mathematical modeling.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 43%
Student > Ph. D. Student 2 29%
Unspecified 1 14%
Lecturer 1 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 29%
Biochemistry, Genetics and Molecular Biology 2 29%
Unspecified 1 14%
Computer Science 1 14%
Neuroscience 1 14%
Other 0 0%