An N-glycopeptide MS/MS data analysis workflow leveraging two complementary glycoproteomic software tools for more confident identification and assignments.

Complete coverage of all N-glycosylation sites on the SARS-CoV2 spike protein would require the use of multiple proteases in addition to trypsin. Subsequent identification of the resulting glycopeptides by searching against database often introduces assignment errors due to similar mass differences between different permutations of amino acids and glycosyl residues. By manually interpreting the individual MS spectra, we report here the common sources of errors in assignment, especially those introduced by the use of chymotrypsin. We show that by applying a stringent threshold of acceptance, erroneous assignment by the commonly used Byonic software can be controlled within 15%, which can be reduced further if only those also confidently identified by a different search engine, pGlyco3, were considered. A representative site-specific N-glycosylation pattern could be constructed based on quantifying only the overlapping subset of N-glycopeptides identified at higher confidence. Applying the two complimentary glycoproteomic software in a concerted data analysis workflow, we found and confirmed that glycosylation at several sites of an unstable Omicron spike protein differed significantly from those of the stable trimeric product of the parental D614G variant.