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Automated methods for improved protein identification by peptide mass fingerprinting

In English

Författare

Summary, in 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.

Publiceringsår

2004

Språk

Engelska

Sidor

2594-2601

Publikation/Tidskrift/Serie

Proteomics

Volym

4

Issue

9

Dokumenttyp

Artikel i tidskrift

Förlag

John Wiley & Sons Inc.

Ämne

  • Immunology in the medical area

Nyckelord

  • protein
  • mass spectrometry
  • automation
  • database searching
  • identification

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1615-9861