Glycosylation is a major post-translational modification of proteins, which plays a vital role in cell-cell interactions and signal transduction. Since the cell surface is covered with glycans and their structures vary greatly depending on the cell type and condition, disease-related alteration in protein glycosylation is expected to be a pathologically relevant biomarker. Recent technological advances in large-scale analysis of glycopeptides, i.e., glycoproteomics using mass spectrometry (MS), have enabled us to discover unique candidates. In particular, software development for MS-based glycoproteomics allows us to determine the detailed glycan structures on proteins. However, there is no standardized way of thinking for evaluating which assignment is truly "identified". To address this issue, we compared commercial (Byonic) and open-resource software (MSFragger-glyco[1], pGlyco3[2], Glyco-Decipher[3], GRable[4]) to evaluate the differences in glycopeptides identified by each software, using a cultured cell line derived from lymphoma (BJAB cells) as a crude glycoprotein sample.

In this study, proteins were extracted from BJAB cells using phase transfer surfactant (PTS) buffer, and approximately 1 mg of the resulting protein was reduced and alkylated. The protein solution was diluted 5-fold and then treated with trypsin at 1/100 of the protein weight at 37°C overnight. Trifluoroacetic acid was then added to a final concentration of 1% to precipitate and remove PTS, and the glycopeptides were purified using an amide column. One-tenth of the sample was used for LC-MS analysis. The obtained data were analyzed using commercial and open resource software, and the differences in glycopeptides identified by each software were evaluated. Among all 2,002 identified glycopeptides, 236 glycopeptides were commonly identified by all software, supporting their plausibility. On the other hand, a total of 799 glycopeptides were identified by only one software, indicating that many glycopeptides are uniquely identified by each analysis software. In this presentation, we will discuss how to evaluate the certainty of these solely found assignments.

References

[1] Polasky et. al., Nat Methods, 17, 1125-1132 (2020), [2] Zeng et. al., Nat Methods, 18,1515-1523 (2021), [3] Fang et. al. Nat. Commun., 7 (2022), [4] Nagai-Okatani et. al.,BioRxiv, (2024)