Comparison of search engine contributions in protein mass fingerprinting for protein identification

Won A. Joo, Jeong Bok Lee, Mira Park, Jae Won Lee, Hyun Jung Kim, Chan Whan Kim

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Peptide mass fingerprinting (PMF) is a valuable method for rapid and high-throughput protein identification using the proteomics approach. Automated search engines, such as Ms-Fit, Mascot, ProFound, and Peptident, have facilitated protein identification through PMF. The potential to obtain a true MS protein identification result depends on the choice of algorithm as well as experimental factors that influence the information content in MS data. When mass spectral data are incomplete and/or have low mass accuracy, the "number of matches" approach may be inadequate for a useful identification. Several studies have evaluated factors influencing the quality of mass spectrometry (MS) experiments. Missed cleavages, post-translational modifications of peptides and contaminants (e.g., keratin) are important factors that can affect the results of MS analyses by influencing the identification process as well as the quality of the MS spectra. We compared search engines frequently used to identify proteins from Homo sapiens and Halobacterium safinarum by evaluating factors, including database and mass tolerance, to develop an improved search engine for PMF This study may provide information to help develop a more effective algorithm for protein identification in each species through PMF.

Original languageEnglish
Pages (from-to)125-130
Number of pages6
JournalBiotechnology and Bioprocess Engineering
Issue number2
Publication statusPublished - 2007 Mar


  • Peptide mass fingerprinting
  • Searching engine
  • Selectivity
  • Sensibility

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Biomedical Engineering


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