Uncovering missing glycans and unexpected fragments with pGlycoNovo for site-specific glycosylation analysis across species

Wen-Feng Zeng, Guoquan Yan, Huan-huan Zhao, Chao Liu and Weiqian Cao ()
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Wen-Feng Zeng: CAS
Guoquan Yan: Fudan University
Huan-huan Zhao: Fudan University
Chao Liu: CAS
Weiqian Cao: Fudan University

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Precision mapping of site-specific glycans using mass spectrometry is vital in glycoproteomics. However, the diversity of glycan compositions across species often exceeds database capacity, hindering the identification of rare glycans. Here, we introduce pGlycoNovo, a software within the pGlyco3 software environment, which employs a glycan first-based full-range Y-ion dynamic searching strategy. pGlycoNovo enables de novo identification of intact glycopeptides with rare glycans by considering all possible monosaccharide combinations, expanding the glycan search space to 16~1000 times compared to non-open search methods, while maintaining accuracy, sensitivity and speed. Reanalysis of SARS Covid-2 spike protein glycosylation data revealed 230 additional site-specific N-glycans and 30 previously unreported O-glycans. pGlycoNovo demonstrated high complementarity to six other tools and superior search speed. It enables characterization of site-specific N-glycosylation across five evolutionarily distant species, contributing to a dataset of 32,549 site-specific glycans on 4602 proteins, including 2409 site-specific rare glycans, and uncovering unexpected glycan fragments.

Date: 2024
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DOI: 10.1038/s41467-024-52099-7

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