Improved identification of O-linked glycopeptides from ETD data with optimized scoring for different charge states and cleavage specificities

This article describes the effect of re-interrogation of electron-transfer dissociation (ETD) data with newly developed analytical tools. MS/MS-based characterization of O-linked glycopeptides is discussed using data acquired from a complex mixture of O-linked glycopeptides, featuring mucin core 1-type carbohydrates with and without sialic acid, as well as after partial deglycosylation to leave only the core GalNAc units (Darula and Medzihradszky in Mol Cell Proteomics 8:2515, 2009). Information content of collision-induced dissociation spectra generated in collision cell (in QqTOF instruments) and in ion traps is compared. Interpretation of the corresponding ETD data using Protein Prospector is also presented. Search results using scoring based on the frequency of different fragment ions occurring in ETD spectra of tryptic peptides are compared with results obtained after ion weightings were adjusted to accommodate differential ion frequencies in spectra of differing charge states or cleavage specificities. We show that the improved scoring is more than doubled the glycopeptide assignments under very strict acceptance criteria. This study illustrates that “old” proteomic data may yield significant new information when re-interrogated with new, improved tools

In-depth Analysis of Tandem Mass Spectrometry Data from Disparate Instrument Types*S⃞

Mass spectrometric analyses of protein digests produce large numbers of fragmentation spectra that are not identified by routine database searching strategies. Some of these spectra could be identified by development of improved search engines. However, many of these spectra represent fragmentation of peptide components bearing modifications that are not routinely considered in database searches. Here we present new software within Protein Prospector that allows comprehensive analysis of data sets by analyzing the data at increasing levels of depth. Analysis of published data sets is presented to illustrate that the software is not biased to any instrument types. The results show that these data sets contain many modified peptides. As well as searching for known modification types, Protein Prospector permits the detection and identification of unexpected or novel modifications by searching for any mass shift within a user-specified mass range to any chosen amino acid(s). Several modifications never previously reported in proteomics data were identified in these standard data sets using this mass modification searching approach