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bioRxiv · 10.64898/2026.07.03.736455

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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Abstract

Glycopeptide enrichment remains a cornerstone in glycoproteomics, but bias and reproducibility issues continue to hinder biological insight and clinical translation. Employing curated glycoproteomics datasets and machine learning, we trained a glycopeptide classifier to recognize N-glycopeptide precursors through mass defect signatures. Integration of the classifier into a data-dependent acquisition framework facilitated real-time prediction of N-glycopeptides from human serum and revealed sex differences in murine plasma fucosylation opening avenues for enrichment-free glycoproteomics.

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Zhang, B., Chau, T. H., Kristina, B. M., Arakawa, H., Kaji, H., Kawahara, R., Ashwood, C., Matsui, Y., Thaysen-Andersen, M.. 2026-07-05. Real-time mass defect-driven prediction of glycopeptide precursors enables enrichment-free serum glycoproteomics. https://doi.org/10.64898/2026.07.03.736455

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