Abstract

Protein identifications, instead of peptide-spectrum matches, constitute the biologically relevant result of shotgun proteomics studies. How to appropriately infer and report protein identifications has triggered a still ongoing debate. This debate has so far suffered from the lack of appropriate performance measures that allow to objectively assess protein inference approaches.This study describes an intuitive, generic and yet formal performance measure and demonstrates how it enables experimentalists to select an optimal protein inference strategy for a given collection of fragment ion spectra. We applied the performance measure to systematically explore the benefit of excluding possibly unreliable protein identifications, such as single hit wonders. Therefore, we defined a family of protein inference engines, by extending a simple inference engine by thousands of pruning variants, each excluding a different specified set of possibly unreliable identifications. We benchmarked these protein inference engines on several datasets representing different proteomes and mass spectrometrical platforms. Optimally performing inference engines retained all high confidence spectral evidence, without posterior exclusion of any type of protein identifications.Despite the diversity of studied datasets consistently supporting this rule, other datasets might behave differently. In order to ensure maximal reliable proteome coverage for datasets arising in other studies, we advocate to abstain from rigid protein inference rules, like exclusion of single hit wonders, and instead to consider several protein inference approaches and to assess these with respect to the presented performance measure in the specific application context.