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Proteomics for Drug Discovery

Overview of attention for book
Cover of 'Proteomics for Drug Discovery'

Table of Contents

  1. Altmetric Badge
    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
  20. Altmetric Badge
    Chapter 19 Bioinformatics Resources for Interpreting Proteomics Mass Spectrometry Data
  21. Altmetric Badge
    Chapter 20 Erratum to: Probing Protein Kinase-ATP Interactions Using a Fluorescent ATP Analog
Attention for Chapter 9: Post-Translational Modification Profiling-Functional Proteomics for the Analysis of Immune Regulation
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Chapter title
Post-Translational Modification Profiling-Functional Proteomics for the Analysis of Immune Regulation
Chapter number 9
Book title
Proteomics for Drug Discovery
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-7201-2_9
Pubmed ID
28809000
Book ISBNs
978-1-4939-7200-5, 978-1-4939-7201-2
Authors

Avital Eisenberg-Lerner, Ifat Regev, Yifat Merbl

Abstract

Posttranslational modifications (PTMs) of proteins are an integral part of major cellular regulatory mechanisms dictating protein function, localization, and stability. The capacity to screen PTMs using protein microarrays has advanced our ability to identify their targets and regulatory role. This chapter discusses a unique procedure that combines functional extract-based activity assay with large-scale screening utilities of protein microarrays. This "PTM-profiling" system offers advantages in quantitatively identifying modifications in an unbiased manner in the context of specific cellular conditions. While the possibilities of studying PTMs in different settings are enormous, the immune system presents an attractive model for studying the effects of perturbations in PTMs, and specifically the ubiquitin system, as these were already implicated in both immune function and dysfunction. This chapter discusses the significance of PTM profiling in addressing basic questions in immunology. We describe detailed protocols for the preparation of functional cell extracts from immune cell cultures, following differentiation or induced signals, and screening PTMs on protein arrays, as well as basic guidelines for data analysis and interpretation.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 23%
Student > Doctoral Student 2 15%
Researcher 2 15%
Student > Ph. D. Student 1 8%
Lecturer > Senior Lecturer 1 8%
Other 2 15%
Unknown 2 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 38%
Chemistry 2 15%
Agricultural and Biological Sciences 1 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Medicine and Dentistry 1 8%
Other 1 8%
Unknown 2 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 25 August 2017.
All research outputs
#14,823,817
of 22,997,544 outputs
Outputs from Methods in molecular biology
#4,660
of 13,151 outputs
Outputs of similar age
#240,417
of 421,208 outputs
Outputs of similar age from Methods in molecular biology
#410
of 1,074 outputs
Altmetric has tracked 22,997,544 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,151 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 421,208 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.

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