Estimating the Confidence of Peptide Identifications without Decoy Databases

Abstract

Using decoy databases to compute the confidence of peptide identifications has become the standard procedure for mass spectrometry driven proteomics. While decoy databases have numerous advantages, they double the run time and are not applicable to all peptide identification problems such as error-tolerant or de novo searches or the large-scale identification of cross-linked peptides. Instead, we propose a fast, simple and robust mixture modeling approach to estimate the confidence of peptide identifications without the need for decoy database searches, which automatically checks whether its underlying assumptions are fulfilled. This approach is then evaluated on 41 LC/MS data sets of varying complexity and origin. The results are very similar to those of the decoy database strategy at a negligible computational cost. Our approach is applicable not only to standard protein identification workflows, but also to proteomics problems for which meaningful decoy databases cannot be constructed.