Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

By Andrew Keller, Alexey I. Nesvizhskii, E. Kolker, R. Aebersold. Analytical Chemistry, 2002

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Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, methodology): “The new customized databases were used to search each corresponding dataset against spectra previously classified as having a PeptideProphet score lower than 0.5.”
The Need for Guidelines in Publication of Peptide and Protein Identification Data (2004, background): “The need for these guidelines is driven in part by the fact that a significant but undefined number of the proteins being reported as “identified” in proteomics articles are likely to be false positives (2).”
DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics (2015, methodology): “For DIA-derived pseudo-MS/MS spectra, PeptideProphet was run separately for each of the three quality categories of MS/MS spectra (QT = 1, 2 or 3).”