4. Methods and Approaches to Mass Spectroscopy-Based Protein Identification

  1. P. David Eckersall2 and
  2. Phillip D. Whitfield3
  1. John D. Lippolis and
  2. Timothy A. Reinhardt

Published Online: 5 AUG 2011

DOI: 10.1002/9780470960660.ch4

Methods in Animal Proteomics

Methods in Animal Proteomics

How to Cite

Lippolis, J. D. and Reinhardt, T. A. (2011) Methods and Approaches to Mass Spectroscopy-Based Protein Identification, in Methods in Animal Proteomics (eds P. D. Eckersall and P. D. Whitfield), Wiley-Blackwell, Oxford, UK. doi: 10.1002/9780470960660.ch4

Editor Information

  1. 2

    University of Glasgow, UK

  2. 3

    University of the Highlands and Islands, UK

Author Information

  1. National Animal Disease Center, IA, USA

Publication History

  1. Published Online: 5 AUG 2011
  2. Published Print: 19 AUG 2011

ISBN Information

Print ISBN: 9780813817910

Online ISBN: 9780470960660

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Keywords:

  • methods and approaches to mass spectroscopy-based protein identification - in biological processes;
  • proteomics, study of protein expression - protein–protein interactions, or posttranslational modifications;
  • goal of proteomics - determining cellular process interdependence, for normal cell growth;
  • mass spectrometers - ionization source, mass analyzers and one or more ion detectors;
  • two predominant methods - ionization of peptides, matrix-assisted laser desorption/ionization (MALDI), and electrospray ionization;
  • mass analyzers, guiding, fragmentation - and scan gas phase ions;
  • sample preparation choices - key to success, UniProtKB/Swiss-Prot Human Proteome Initiative;
  • expression proteomics experiment - using iTRAQ labeling;
  • linking mass spectra with proteins - proteomic experiment, and large quantities of MS data;
  • MS data validation - using traditional protein chemistry methods of protein detection

Summary

This chapter contains sections titled:

  • Introduction

  • MS

  • Sample Preparation

  • Protein Identification

  • Linking Mass Spectra with Proteins

  • Validation of MS Data

  • Conclusions

  • References