Identification of tryptic peptides from large databases using multiplexed tandem mass spectrometry: Simulations and experimental results

Christophe Masselon, Ljiljana Paša-Tolić, Sang Won Lee, Lingjun Li, Gordon A. Anderson, Richard Harkewicz, Richard D. Smith

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

Multiplexed tandem mass spectrometry (MS/MS) has recently been demonstrated as a means to increase the throughput of peptide identification in liquid chromatography (LC) MS/MS experiments. In this approach, a set of parent species is dissociated simultaneously and measured in a single spectrum (in the same manner that a single parent ion is conventionally studied), providing a gain in sensitivity and throughput proportional to the number of species that can be simultaneously addressed. In the present work, simulations performed using the Caenorhabditis elegans predicted proteins database show that multiplexed MS/MS data allow the identification of tryptic peptides from mixtures of up to ten peptides from a single dataset with only three "y" or "b" fragments per peptide and a mass accuracy of 2.5 to 5 ppm. At this level of database and data complexity, 98% of the 500 peptides considered in the simulation were correctly identified. This compares favorably with the rates obtained for classical MS/MS at more modest mass measurement accuracy. LC multiplexed Fourier transform-ion cyclotron resonance MS/MS data obtained from a 66 kDa protein (bovine serum albumin) tryptic digest sample are presented to illustrate the approach, and confirm that peptides can be effectively identified from the C. elegans database to which the protein sequence had been appended.

Original languageEnglish
Pages (from-to)1279-1286
Number of pages8
JournalProteomics
Volume3
Issue number7
DOIs
Publication statusPublished - 2003 Jul 1
Externally publishedYes

Keywords

  • Fourier transform ion cyclotron resonance
  • Liquid chromatography

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

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