Nonredundant mass spectrometry: a strategy to integrate mass spectrometry acquisition and analysis

Proteomics. 2004 Apr;4(4):917-27. doi: 10.1002/pmic.200300673.

Abstract

Protein identification using automated data-dependent tandem mass spectrometry (MS/MS) is now a standard procedure. However, in many cases data-dependent acquisition becomes redundant acquisition as many different peptides from the same protein are fragmented, whilst only a few are needed for unambiguous identification. To increase the quality of information but decrease the amount of information, a nonredundant MS (nrMS) strategy has been developed. With nrMS, data analysis is an integral part of the overall MS acquisition and analysis, and not an endpoint as typically performed. In this nrMS workflow a matrix assisted laser desorption/ionization-time of flight-time of flight (MALDI-TOF/TOF) instrument is used. MS and restricted MS/MS data are searched and identified proteins are used to generate an "exclusion list", after in silico digestion. Peptide fragmentation is then restricted to only the most intense ions not present in the exclusion list. This process is repeated until all peaks are accounted for or the sample is consumed. Compared to nanoLC-MS/MS, nrMS yielded similar results for the analysis of six pooled two-dimensional electrophoresis (2-DE) spots. In comparison to standard data-dependent MALDI-MS/MS for sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel band analysis, nrMS dramatically increased the number of identified proteins. It was also found that this new workflow significantly increased sequence coverage by identifying unexpected peptides, which can result from post-translational modifications.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Amino Acid Sequence
  • Databases, Protein
  • Electrophoresis, Gel, Two-Dimensional
  • Molecular Sequence Data
  • Peptide Mapping*
  • Proteome*
  • Software*
  • Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
  • Staphylococcus aureus / metabolism
  • Statistics as Topic

Substances

  • Proteome