Properties of isotope patterns and their utility for peptide identification in large-scale proteomic experiments

Abstract

Rationale: In proteomic experiments, isotope patterns are routinely generated for all detected peptides. While this pattern is determined by peptide composition, it has not been evaluated as a parameter that can help in the process of peptide identification.

Methods: First, we investigated how the relative isotope abundance (RIA) accuracy in proteomic data sets depends on the spectral intensity, resolution, and the number of mass spectrometry (MS) 1 scans, using an Orbitrap Velos mass spectrometer. Next, we explored the discriminatory power of isotope patterns in the context of proteome analyses of various complexities, either alone or in combination with a Mascot database search. Finally, we provide a theoretical framework for the required accuracies of both peptide mass and RIA for peptide identification.

Results: We demonstrate that the RIA error obtained in routine proteome analyses is 4-5%, and that this is only modestly influenced by spectral intensity, resolution, and the number of MS1 scans. While RIA alone has no discriminatory power, in a Mascot search isotope patterns can distinguish top scoring hits from runner-up hits in 70-95% of cases. Our theoretical approach shows that RIA accuracy needs to be ~0.2% in order to uniquely identify peptides in full proteomes.

Conclusions: Our results demonstrate that isotope patterns can have discriminatory power when used in combination with a classical database search. Inclusion of this parameter in proteomic workflows may help to increase confidence in peptide identification, but in practical terms this will be limited to small proteomes.