Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

We assess the ability of open searching (OS) to analyze bacterial glycoproteomes. We find OS readily identifies known and unknown glycans within samples. Using OS, we compared glycan use across eight Burkholderia species confirming the conservation of glycan use and highlight how low frequency glycans can be identified in OS data sets. Importantly, OS is not without caveats; leading to lower assignment scores and under reporting multiply glycosylated peptides. This supports the value of OS followed by focused searching approaches. Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach usingN-linked glycopeptides ofCampylobacter fetus subsp. fetusas well asO-linked glycopeptides ofAcinetobacter baumanniiandBurkholderia cenocepaciarevealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.