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

Accurate identification of intact glycopeptides from mass spectrometry data is essential for the characterization of glycosylation events in biological samples. Here, the authors propose GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides. 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.

Automated summary

GlycanFinder is a database search and de novo sequencing tool for accurate identification of intact glycopeptides. It integrates peptide-based and glycan-based search strategies to overcome the challenge of complex fragmentation. With a deep learning model, GlycanFinder is able to sequence glycans not found in the database. Extensive analyses and benchmarking studies demonstrate its comparable performance to leading software and its capability to identify previously unknown glycopeptides.