False Discovery Rates of Protein Identifications: A Strike against the Two-Peptide Rule

Abstract

Most proteomics studies attempt to maximize the number of peptide identifications and subsequently infer proteins containing two or more peptides as reliable protein identifications. In this study, we evaluate the effect of this “two-peptide” rule on protein identifications, using multiple search tools and data sets. Contrary to the intuition, the “two-peptide” rule reduces the number of protein identifications in the target database more significantly than in the decoy database and results in increased false discovery rates, compared to the case when single-hit proteins are not discarded. We therefore recommend that the “two-peptide” rule should be abandoned, and instead, protein identifications should be subject to the estimation of error rates, as is the case with peptide identifications. We further extend the generating function approach (originally proposed for evaluating matches between a peptide and a single spectrum) to evaluating matches between a protein and an entire spectral data set.

Direct Maximization of Protein Identifications from Tandem Mass Spectra

“…This example suggests that Barista is less biased against proteins with a single good identification than ProteinProphet, which penalizes the tubulin ␤ protein group more stringently for the presence of peptides with low probability scores. The validity of this identification by Barista agrees with recent evidence that requiring at least two peptides per protein unnecessarily eliminates many true identifications (36).…”

Linkage of Oxidative Stress and Mitochondrial Dysfunctions to Spontaneous Culture Degeneration in Aspergillus nidulans

“…The peptides were discarded for further analysis if mapped to more than two different proteins. A protein was considered identifiable with at least one unique peptide detected for more than twice in the three replicates of each sample (29). Full MS data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.…”

From hundreds to thousands: Widening the normal human Urinome (1)

“…It was developed a new strategy of analysis which allowed the extension of the number of identified proteins to 3429 (1724 were of new description here). The list of identified proteins is reported in Supplementary Raw MS files as processed with the Thermo Scientific Proteome Discoverer software: peak were searched by the MASCOT and SEQUEST against Uniprot human database, filtered for a maximum 1% FDR using Percolator; the Peptide Mass Deviation was set to 10 ppm and a minimum of six 6 amino acids per identified peptide were required [2,3]. The Database search parameters were mass tolerance precursor 20 ppm [4], mass tolerance fragment CID 0.8 Da with dynamic modification of deamidation (N, Q), oxidation (M) and static modification of alkylation with IAM (C).…”