Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics

Weiping Sun, Qianqiu Zhang, Xiyue Zhang, Ngoc Hieu Tran, M. Ziaur Rahman, Zheng Chen, Chao Peng, Jun Ma, Ming Li (), Lei Xin () and Baozhen Shan ()
Additional contact information
Weiping Sun: Bioinformatics Solutions Inc.
Qianqiu Zhang: David R. Cheriton School of Computer Science, University of Waterloo
Xiyue Zhang: Bioinformatics Solutions Inc.
Ngoc Hieu Tran: Bioinformatics Solutions Inc.
M. Ziaur Rahman: Bioinformatics Solutions Inc.
Zheng Chen: Bioinformatics Solutions Inc.
Chao Peng: BaizhenBio Inc.
Jun Ma: Bioinformatics Solutions Inc.
Ming Li: David R. Cheriton School of Computer Science, University of Waterloo
Lei Xin: Bioinformatics Solutions Inc.
Baozhen Shan: Bioinformatics Solutions Inc.

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learning model is designed to capture glycan tree structures and their fragment ions for de novo sequencing of glycans that do not exist in the database. We performed extensive analyses to validate the false discovery rates (FDRs) at both peptide and glycan levels and to evaluate GlycanFinder based on comprehensive benchmarks from previous community-based studies. Our results show that GlycanFinder achieved comparable performance to other leading glycoproteomics softwares in terms of both FDR control and the number of identifications. Moreover, GlycanFinder was also able to identify glycopeptides not found in existing databases. Finally, we conducted a mass spectrometry experiment for antibody N-linked glycosylation profiling that could distinguish isomeric peptides and glycans in four immunoglobulin G subclasses, which had been a challenging problem to previous studies.

Date: 2023
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DOI: 10.1038/s41467-023-39699-5

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