Monte carlo simulation-based algorithms for analysis of shotgun proteomic data

J Proteome Res. 2008 Jul;7(7):2605-15. doi: 10.1021/pr800002u. Epub 2008 Jun 11.


Two new statistical models based on Monte Carlo Simulation (MCS) have been developed to score peptide matches in shotgun proteomic data and incorporated in a database search program, MassMatrix ( The first model evaluates peptide matches based on the total abundance of matched peaks in the experimental spectra. The second model evaluates amino acid residue tags within MS/MS spectra. The two models provide complementary scores for peptide matches that result in higher confidence in peptide identification when significant scores are returned from both models. The MCS-based models use a variance reduction technique that improves estimation precision. Due to the high computational expense of MCS-based models, peptide matches were prefiltered by other statistical models before further evaluation by the MCS-based models. Receiver operating characteristic analysis of the data sets confirmed that MCS-based models improved the overall performance of the MassMatrix search software, especially for low-mass accuracy data sets.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acids / chemistry
  • Databases, Factual
  • Electricity
  • Models, Statistical*
  • Monte Carlo Method
  • Peptides / chemistry
  • Proteomics / methods*
  • ROC Curve
  • Tandem Mass Spectrometry


  • Amino Acids
  • Peptides