Table 1.

Comparison of the advantages and disadvantages of the most commonly used search engines for peptide and protein identification.

Search Engine	Method	Advantages	Disadvantages
Mascot	Uses a probability modelling algorithm and protein database searching. Matches experimental peptide and fragment ion masses to ones generated in silico from databases.	User-friendly interface. Provides an error-tolerant search facility. Sophisticated but complex data export possibilities.	Very reliant on user input for correct identification of oxPTMs, otherwise false positives and negatives occur.
Sequest	Uses an algorithm based on a cross correlation function, plus protein data base searching. Matches experimental peptide and fragment ion masses to ones generated in silico from databases.	User-friendly interface. Provides an error-tolerant search facility.	Very reliant on user input for correct identification of oxPTMs, otherwise false positives and negatives occur.
ProteinPilot	Uses a sequence tag method plus protein database searching.	User-friendly interface. Potentially better at identifying unsuspected modifications.	If the initial sequence tag is incorrectly identified, the experimental peptide will not be matched to the correct peptide. Long analysis run times.
pMatch	Spectral library searching against experimentally-derived data.	Has been reported to be better at identifying PTMs, and specifically at coping with the unusual fragmentation of peptides caused by PTMs.	Since this method uses a spectral library, the peptide will only be identified if the spectra are available in the spectral library.
MS Amanda	Based on a binomial distribution function. Protein data base searching. Matches experimental peptide and fragment ion masses to ones generated in silico from databases.	Reported to be better at identifying peptides of higher m/z than Mascot and Sequest.	Very reliant on user input for correct identification of oxPTMs, otherwise false positives and negatives occur.