Real-time mass defect-driven prediction of glycopeptide precursors enables enrichment-free serum glycoproteomics
Zhang, B.; Chau, T. H.; Kristina, B. M.; Arakawa, H.; Kaji, H.; Kawahara, R.; Ashwood, C.; Matsui, Y.; Thaysen-Andersen, M.
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Glycopeptide enrichment remains a cornerstone in glycoproteomics, but bias and reproducibility issues continue to hinder biological insight and clinical translation. Using curated glycoproteomics datasets and machine learning, we trained a glycopeptide classifier to promptly recognize N-glycopeptide precursor ions in peptide mixtures through mass defect signatures. Integration of the classifier into a data-dependent acquisition framework facilitated efficient and unbiased real-time prediction of N-glycopeptides directly from serum opening avenues for enrichment-free glycoproteomics.
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