When using SEQUEST, what represents a good score? When using Mascot, what is the trade-off of missed identifications to false positives? These are hard questions whose answers depend on the instrument, sample complexity, FASTA database size, and experimental design. The PeptideProphet algorithm gives the answers.

PeptideProphet™ was developed by Keller, Nesvizhskii, et al.¹ at The Institute for Systems Biology as a Bayesian statistical algorithm to convert SEQUEST scores into probabilities. The creators of PeptideProphet™ have written several articles^2,3 explaining the advantages of this probability scheme.

References

1. Keller, A., Nesvizhskii, A. I., Kolker, E., and Aebersold R., "Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search." Anal. Chem. 2002 Oct 15;74(20):5383-92. PubMed

2. Nesvizhskii, A. I., and Aebersold, R., "Analysis, statistical validation and dissemination of large-scale proteomics datasets generated by tandem MS." Drug Discov. Today. 2004 Feb 15;9(4):173-81. PubMed

3. Carr, S., Aebersold, R., Baldwin, M., Burlingame, A., Clauser, K., and Nesvizhskii, A., "The need for guidelines in publication of peptide and protein identification data: Working Group on Publication Guidelines for Peptide and Protein Identification Data", Mol. Cell Proteomics. 2004 Jun;3(6):531-3. Epub 2004 Apr 09. PubMed

4. Qian W, Liu T, Monroe M, Strittmatter E, Jacobs J, Kangas L, Petritis K, Camp D, Smith R, "Probability-Based Evaluation of Peptide and Protein Identifications from Tandem Mass Spectrometry and SEQUEST Analysis: The Human Proteome." Journal of Proteome Research, 2005 Jan-Feb; 4(1):53-62. PubMed