Recent trends in glycoproteomics by characterization of intact glycopeptides

Electron-Activated Dissociation and Collision-Induced Dissociation Glycopeptide Fragmentation for Improved Glycoproteomics

Tandem mass spectrometry coupled with liquid chromatography (LC-MS/MS) has proven a versatile tool for the identification and quantification of proteins and their post-translational modifications (PTMs). Protein glycosylation is a critical PTM for the stability and biological function of many proteins, but full characterization of site-specific glycosylation of proteins remains analytically challenging. Collision-induced dissociation (CID) is the most common fragmentation method used in LC-MS/MS workflows, but the loss of labile modifications renders CID inappropriate for detailed characterization of site-specific glycosylation. Electron-based dissociation methods provide alternatives that retain intact glycopeptide fragments for unambiguous site localization, but these methods often underperform CID due to increased reaction times and reduced efficiency. Electron-activated dissociation (EAD) is another strategy for glycopeptide fragmentation. Here, we use a ZenoTOF 7600 SCIEX instrument to compare the performance of various fragmentation techniques for the analysis of a complex mixture of mammalian O- and N-glycopeptides. We found CID fragmentation identified the most glycopeptides and generally produced higher quality spectra, but EAD provided improved confidence in glycosylation site localization. Supplementing EAD with CID fragmentation (EAciD) further increased the number and quality of glycopeptide identifications, while retaining localization confidence. These methods will be useful for glycoproteomics workflows for either optimal glycopeptide identification or characterization.

Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine

High confidence and reproducibility are still challenges in bottom-up mass spectrometric N-glycopeptide identification. The collision energy used in the MS/MS measurements and the database search engine used to identify the species are perhaps the two most decisive factors. We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering the highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied and studied the behavior of Byonic, pGlyco, and GlycoQuest scores. We found that search engines show a range of behavior between peptide-centric and glycan-centric, which manifests itself already in the dependence of optimal collision energy on m/z. Using classical statistical and machine learning methods, we revealed that peptide hydrophobicity, glycan and peptide masses, and the number of mobile protons also have significant and search-engine-dependent influence, as opposed to a series of other parameters we probed. We envisioned an MS/MS workflow making a smart collision energy choice based on online available features such as the hydrophobicity (described by retention time) and glycan mass (potentially available from a scout MS/MS). Our assessment suggests that this workflow can lead to a significant gain (up to 100%) in the identification confidence, particularly for low-scoring hits close to the filtering limit, which has the potential to enhance reproducibility of N-glycopeptide analyses. Data are available via MassIVE (MSV000093110).