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Proteomics for Drug Discovery

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Cover of 'Proteomics for Drug Discovery'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 A Photoaffinity Labeling-Based Chemoproteomics Strategy for Unbiased Target Deconvolution of Small Molecule Drug Candidates
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    Chapter 2 Multiplexed Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry Quantification of Cancer Signaling Proteins
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    Chapter 3 Monitoring Dynamic Changes of the Cell Surface Glycoproteome by Quantitative Proteomics
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    Chapter 4 High-Resolution Parallel Reaction Monitoring with Electron Transfer Dissociation for Middle-Down Proteomics: An Application to Study the Quantitative Changes Induced by Histone Modifying Enzyme Inhibitors and Activators
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    Chapter 5 Preparation and Immunoaffinity Depletion of Fresh Frozen Tissue Homogenates for Mass Spectrometry-Based Proteomics in the Context of Drug Target/Biomarker Discovery
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    Chapter 6 Target Identification Using Cell Permeable and Cleavable Chloroalkane Derivatized Small Molecules
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    Chapter 7 Microfluidics-Mass Spectrometry of Protein-Carbohydrate Interactions: Applications to the Development of Therapeutics and Biomarker Discovery
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    Chapter 8 Studying Protein–Protein Interactions by Biotin AP-Tagged Pulldown and LTQ-Orbitrap Mass Spectrometry
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    Chapter 9 Post-Translational Modification Profiling-Functional Proteomics for the Analysis of Immune Regulation
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    Chapter 10 Reverse Phase Protein Arrays and Drug Discovery
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    Chapter 11 Probing Protein Kinase-ATP Interactions Using a Fluorescent ATP Analog
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    Chapter 12 Preparation of Disease-Related Protein Assemblies for Single Particle Electron Microscopy
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    Chapter 13 Identification of Lipid Binding Modulators Using the Protein-Lipid Overlay Assay
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    Chapter 14 Resazurin Live Cell Assay: Setup and Fine-Tuning for Reliable Cytotoxicity Results
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    Chapter 15 Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows
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    Chapter 16 Method to Identify Silent Codon Mutations That May Alter Peptide Elongation Kinetics and Co-translational Protein Folding
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    Chapter 17 In Silico Design of Anticancer Peptides
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    Chapter 18 Docking and Virtual Screening in Drug Discovery
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    Chapter 19 Bioinformatics Resources for Interpreting Proteomics Mass Spectrometry Data
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    Chapter 20 Erratum to: Probing Protein Kinase-ATP Interactions Using a Fluorescent ATP Analog
Attention for Chapter 8: Studying Protein–Protein Interactions by Biotin AP-Tagged Pulldown and LTQ-Orbitrap Mass Spectrometry
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Chapter title
Studying Protein–Protein Interactions by Biotin AP-Tagged Pulldown and LTQ-Orbitrap Mass Spectrometry
Chapter number 8
Book title
Proteomics for Drug Discovery
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-7201-2_8
Pubmed ID
Book ISBNs
978-1-4939-7200-5, 978-1-4939-7201-2
Authors

Zhongqiu Xie, Yuemeng Jia, Hui Li

Abstract

The study of protein-protein interactions represents a key aspect of biological research. Identifying unknown protein binding partners using mass spectrometry (MS)-based proteomics has evolved into an indispensable strategy in drug discovery. The classic approach of immunoprecipitation with specific antibodies against the proteins of interest has limitations, such as the need for immunoprecipitation-qualified antibody. The biotin AP-tag pull-down system has the advantage of high specificity, ease of use, and no requirement for antibody. It is based on the high specificity, high affinity interaction between biotin and streptavidin. After pulldown, in-gel tryptic digestion and tandem mass spectrometry (MS/MS) analysis of sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) protein bands can be performed. In this work, we provide protocols that can be used for the identification of proteins that interact with FOXM1, a protein that has recently emerged as a potential biomarker and drug target in oncotherapy, as an example. We focus on the pull-down procedure and assess the efficacy of the pulldown with known FOXM1 interactors such as β-catenin. We use a high performance LTQ Orbitrap MSn system that combines rapid LTQ ion trap data acquisition with high mass accuracy Orbitrap analysis to identify the interacting proteins.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 25%
Student > Ph. D. Student 3 25%
Unspecified 1 8%
Researcher 1 8%
Student > Master 1 8%
Other 0 0%
Unknown 3 25%
Readers by discipline Count As %
Chemistry 4 33%
Biochemistry, Genetics and Molecular Biology 2 17%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Agricultural and Biological Sciences 1 8%
Unspecified 1 8%
Other 0 0%
Unknown 3 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 August 2017.
All research outputs
#20,442,790
of 22,997,544 outputs
Outputs from Methods in molecular biology
#9,933
of 13,151 outputs
Outputs of similar age
#356,128
of 421,208 outputs
Outputs of similar age from Methods in molecular biology
#842
of 1,074 outputs
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