Menu

Javascript is not activated in your browser. This website needs javascript activated to work properly.
You are here

Automated methods for improved protein identification by peptide mass fingerprinting

Author:
  • Fredrik Levander
  • T Rognvaldsson
  • J Samuelsson
  • Peter James
Publishing year: 2004
Language: English
Pages: 2594-2601
Publication/Series: Proteomics
Volume: 4
Issue: 9
Document type: Journal article
Publisher: John Wiley & Sons

Abstract english

In order to maximize protein identification by peptide mass fingerprinting noise peaks must be removed from spectra and recalibration is often required. The preprocessing of the spectra before database searching is essential but is time-consuming. Nevertheless, the optimal database search parameters often vary over a batch of samples. For high-throughput protein identification, these factors should be set automatically, with no or little human intervention. In the present work automated batch filtering and recalibration using a statistical filter is described. The filter is combined with multiple data searches that are performed automatically. We show that, using several hundred protein digests, protein identification rates could be more than doubled, compared to standard database searching. Furthermore, automated large-scale in-gel digestion of proteins with endoproteinase LysC, and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) analysis, followed by subsequent trypsin digestion and MALDI-TOF analysis were performed. Several proteins could be identified only after digestion with one of the enzymes, and some less significant protein identifications were confirmed after digestion with the other enzyme. The results indicate that identification of especially small and low-abundance proteins could be significantly improved after sequential digestions with two enzymes.

Keywords

  • Immunology in the medical area
  • protein
  • mass spectrometry
  • automation
  • database searching
  • identification

Other

Published
  • ISSN: 1615-9861
Peter James
E-mail: peter [dot] james [at] immun [dot] lth [dot] se

Professor

Department of Immunotechnology

+46 46 222 14 96

+46 70 247 79 60

MV406411E1

90