Abstract
Determining which glycan moieties occupy specific N-glycosylation sites is a highly challenging analytical task. Arguably, the most common approach involves LC-MS and LC-MS/MS analysis of glycopeptides generated by proteases with high cleavage site specificity; however, the depth achieved by this approach is modest. Nonglycosylated peptides are a major challenge to glycoproteomics, as they are preferentially selected for data-dependent MS/MS due to higher ionization efficiencies and higher stoichiometric levels in moderately complex samples. With the goal of improving glycopeptide coverage, a mass defect classifier was developed that discriminates between peptides and glycopeptides in complex mixtures based on accurate mass measurements of precursor peaks. By using the classifier, glycopeptides that were not fragmented in an initial data-dependent acquisition run may be targeted in a subsequent analysis without any prior knowledge of the glycan or protein species present in the mixture. Additionally, from probable glycopeptides that were poorly fragmented, tandem mass spectra may be reacquired using optimal glycopeptide settings. We demonstrate high sensitivity (0.892) and specificity (0.947) based on an in silico dataset spanning >100,000 tryptic entries. Comparable results were obtained using chymotryptic species. Further validation using published data and a fractionated tryptic digest of human urinary proteins was performed, yielding a sensitivity of 0.90 and a specificity of 0.93. Lists of glycopeptides may be generated from an initial proteomics experiment, and we show they may be efficiently targeted using the classifier. Considering the growing availability of high accuracy mass analyzers, this approach represents a simple and broadly applicable means of increasing the depth of MS/MS-based glycoproteomic analyses.
Highlights
N-Glycosylation is an important post-translational modification that affects cell-cell signaling and protein stability, and it has been implicated in various pathologies [1]
As mass defect (MD) classifications have been applied to similar challenges in proteomics [11,12,13], we investigated whether an MD classification would be useful for discriminating between peptides and glycopeptides
A sensitive and specific method applied to generic proteomic data that discriminates N-glycopeptides from nonglycosylated peptides based on accurate mass measurements has been developed
Summary
N-Glycosylation is an important post-translational modification that affects cell-cell signaling and protein stability, and it has been implicated in various pathologies [1]. As noted in a recent publication, glycopeptides are often not selected for fragmentation in data-dependent analysis (DDA)1 [6], making glycopeptide identification impossible, as fragmentation is required for glycopeptide identification in nontrivial samples [7] To circumvent this issue, glycopeptide enrichment protocols using normal-phase hydrophilic interaction chromatography or lectin enrichment techniques have been established to enrich for glycopeptides [8]. The abbreviations used are: DDA, data-dependent analysis; MD, mass defect; GRAEZ, glycopeptide-rich acquisition enhancement zone; TEAB, triethyl ammonium bicarbonate; ACG, automatic gain control Because of these challenges, a classifier capable of quickly discriminating between peptide and glycopeptide signals in mass spectrometry would be valuable and may significantly complement existing purification techniques. No studies have determined whether the MD shift holds for peptides and glycopeptides generated by the same protease, a more pertinent comparison given typical sample preparation protocols
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