Literature DB >> 23153790

Label-free quantitative proteomics trends for protein-protein interactions.

Stephen Tate1, Brett Larsen, Ron Bonner, Anne-Claude Gingras.   

Abstract

Understanding protein interactions within the complexity of a living cell is challenging, but techniques coupling affinity purification and mass spectrometry have enabled important progress to be made in the past 15 years. As identification of protein-protein interactions is becoming easier, the quantification of the interaction dynamics is the next frontier. Several quantitative mass spectrometric approaches have been developed to address this issue that vary in their strengths and weaknesses. While isotopic labeling approaches continue to contribute to the identification of regulated interactions, techniques that do not require labeling are becoming increasingly used in the field. Here, we describe the major types of label-free quantification used in interaction proteomics, and discuss the relative merits of data dependent and data independent acquisition approaches in label-free quantification. This article is part of a Special Issue entitled: From protein structures to clinical applications.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 23153790     DOI: 10.1016/j.jprot.2012.10.027

Source DB:  PubMed          Journal:  J Proteomics        ISSN: 1874-3919            Impact factor:   4.044


  23 in total

1.  Simultaneous Quantification of Viral Antigen Expression Kinetics Using Data-Independent (DIA) Mass Spectrometry.

Authors:  Nathan P Croft; Danielle A de Verteuil; Stewart A Smith; Yik Chun Wong; Ralf B Schittenhelm; David C Tscharke; Anthony W Purcell
Journal:  Mol Cell Proteomics       Date:  2015-03-09       Impact factor: 5.911

2.  Systematic evaluation of data-independent acquisition for sensitive and reproducible proteomics-a prototype design for a single injection assay.

Authors:  Michael R Heaven; Adam J Funk; Archie L Cobbs; Wendy D Haffey; Jeremy L Norris; Robert E McCullumsmith; Kenneth D Greis
Journal:  J Mass Spectrom       Date:  2016-01       Impact factor: 1.982

3.  Human Leukocyte Antigen (HLA) B27 Allotype-Specific Binding and Candidate Arthritogenic Peptides Revealed through Heuristic Clustering of Data-independent Acquisition Mass Spectrometry (DIA-MS) Data.

Authors:  Ralf B Schittenhelm; Saranjah Sivaneswaran; Terry C C Lim Kam Sian; Nathan P Croft; Anthony W Purcell
Journal:  Mol Cell Proteomics       Date:  2016-02-29       Impact factor: 5.911

4.  Quantitative profiling of the protein coronas that form around nanoparticles.

Authors:  Dominic Docter; Ute Distler; Wiebke Storck; Jörg Kuharev; Desirée Wünsch; Angelina Hahlbrock; Shirley K Knauer; Stefan Tenzer; Roland H Stauber
Journal:  Nat Protoc       Date:  2014-07-31       Impact factor: 13.491

5.  Label-free quantification in ion mobility-enhanced data-independent acquisition proteomics.

Authors:  Ute Distler; Jörg Kuharev; Pedro Navarro; Stefan Tenzer
Journal:  Nat Protoc       Date:  2016-03-24       Impact factor: 13.491

6.  A relative quantitative positive/negative ion switching method for untargeted lipidomics via high resolution LC-MS/MS from any biological source.

Authors:  Susanne B Breitkopf; Stéphane J H Ricoult; Min Yuan; Ying Xu; David A Peake; Brendan D Manning; John M Asara
Journal:  Metabolomics       Date:  2017-02-07       Impact factor: 4.290

7.  Determining the Composition and Stability of Protein Complexes Using an Integrated Label-Free and Stable Isotope Labeling Strategy.

Authors:  Todd M Greco; Amanda J Guise; Ileana M Cristea
Journal:  Methods Mol Biol       Date:  2016

8.  DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics.

Authors:  Chih-Chiang Tsou; Dmitry Avtonomov; Brett Larsen; Monika Tucholska; Hyungwon Choi; Anne-Claude Gingras; Alexey I Nesvizhskii
Journal:  Nat Methods       Date:  2015-01-19       Impact factor: 28.547

9.  Utilising proteomic approaches to understand oncogenic human herpesviruses (Review).

Authors:  Christopher B Owen; David J Hughes; Belinda Baquero-Perez; Anja Berndt; Sophie Schumann; Brian R Jackson; Adrian Whitehouse
Journal:  Mol Clin Oncol       Date:  2014-07-09

10.  Steap4 attenuates high glucose and S100B-induced effects in mesangial cells.

Authors:  Chao-Tang Chuang; Jinn-Yuh Guh; Chi-Yu Lu; Yeng-Tseng Wang; Hung-Chun Chen; Lea-Yea Chuang
Journal:  J Cell Mol Med       Date:  2015-03-27       Impact factor: 5.310

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