Benchmarking Glycoproteomics Software Using Diagnostic Ion Based Post-Validation for Glycopeptide Identification

However, algorithmic differences cause discrepancies in glycopeptide identifications even when identical data sets are used. We compared five state-of-the-art glycoproteomics software programs (Byonic, MSFragger-Glyco, pGlyco3, Glyco-Decipher, and GRable), investigating their ability to identify rare NeuGc- and KDN-containing glycopeptides from BJAB-K20 cells, which lack UDP-N-acetylglucosamine 2-epimerase, the rate-limiting enzyme for sialic acid synthesis.

Approximately one-third of the identified glycopeptides were unique to individual tools. Byonic identified the most glycopeptides, whereas Glyco-Decipher and GRable identified complex, highly branched glycans. NeuGc- and KDN-containing glycopeptides were identified by specific programs, highlighting their ability to handle rare glycan structures.