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

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.