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

Sun, Weixuan; Zhang, Qianqiu; Zhang, Xiyue; Tran, Ngoc Hieu; Rahman, M. M. Hafizur; Chen, Jake Y.; Peng, Chao; Ma, Jun; Li, Ming; Lei, Xin; Shan, Baozhen

doi:10.1038/s41467-023-39699-5

Cited by 10 publications

(7 citation statements)

References 50 publications

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“…However, these two N-glycans were reported to be modified with galactose sulfation instead of GlcNAc sulfation. As reported by She et al, the detection of galactose sulfation in intact N-glycopeptides relies on the manual identification of Y ions holding terminal galactose sulfation (50). Our data analysis does not provide any probative structural information on galactose sulfation, for several reasons.…”

Section: New Hexnac-sulfated N-glycans Discovered With the Analysis O...mentioning

confidence: 69%

Immunoglobulin A Carries SulfatedN-glycans Primarily at the Tailpiece Site – An Oxonium-Ion-Guided Approach for Site-SpecificN-glycan Identification

Zuniga-Banuelos,

Lemke,

Hoffmann

et al. 2024

Preprint

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SulfatedN-glycans from human immunoglobulin A (IgA) were recently discovered via glycomic approaches. However, their site-specific description is still pending. CertainN-glycan structures at specificN-glycosylation sites in IgA are crucial for microbial neutralization and effector functions. For instance, sialylatedN-glycans on the C-terminal tailpiece mediate anti-viral activity by interfering with sialic-acid-binding viruses. SulfatedN-glycan epitopes can be ligands for viral proteins and thus play a role in the immune response. In this study, we performed a site-specific screening for sulfatedN-glycans in two commercially available human serum IgA samples employing an in-depthN-glycoproteomic approach, previously developed by us. We found evidence of complex-type and hybrid-typeN-glycans containing sulfatedN-acetylhexosamine (sulfated HexNAc) attached to theN-glycosylation sites in the tailpiece and the CH2 domain of both IgA subclasses. A detailed comparison of theN-glycosylation profiles of human serum IgA samples from two suppliers showed suchN-glycans with sulfated HexNAc consistently in higher abundance in the tailpiece region. Surprisingly, also complex-typeN-glycan compositions bearingO-acetylated sialic acid were identified in the tailpiece. These findings have not been described before for a site-specific glycopeptide analysis. Overall, our work provides a methodology for performing a dedicated site-specific search for sulfated andO-acetylatedN-glycans that can be easily transferred, e.g. to human IgA derived from mucosal tissues, milk, or saliva. Our future aim is to include sulfatedN-glycans into longitudinal studies of IgAN-glycosylation and to investigate their role as a biomarker and a treatment option.Abstract Figure

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Section: New Hexnac-sulfated N-glycans Discovered With the Analysis O...mentioning

confidence: 69%

Immunoglobulin A Carries SulfatedN-glycans Primarily at the Tailpiece Site – An Oxonium-Ion-Guided Approach for Site-SpecificN-glycan Identification

Zuniga-Banuelos,

Lemke,

Hoffmann

et al. 2024

Preprint

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“…With spectrum prediction, we resolved the limitation of spectral library searching on the incomplete library coverage of glycan structure space, and showed its potential for validating or supplementing the structural identifications of glycopeptide by other methods. We further envision that spectrum prediction may improve scoring in glycopeptide database searching and de novo sequencing 57 .…”

Section: Discussionmentioning

confidence: 99%

“…This results in merged spectra containing not only the peptide fragments (b/y ions) but glycan fragments (B/Y ions), which are not covered by the existing models for peptide fragment spectrum prediction. Novel architectures, like graph neural networks that were recently adopted for de novo sequencing of glycans 57 , are required to learn glycan structures and their relevance to the fragment ions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of glycopeptide fragment mass spectra by deep learning

Yang,

Fang

2024

Nat Commun

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Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.

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“…We further performed rescoring by DeepGP-based spectra-matching to explore the value of DeepGP in glycoproteomics. A database entrapment method was adopted to evaluate the glycopeptide identification sensitivity by rescoring, referred to the work by Sun et al 41 , Zeng et al 12 and Liu et al 11 . Fission yeast glycoproteome datasets (Yeast_1, Yeast_2, Supplementary Table 1) were firstly searched by pGlyco3 against a large glycan database, which encompasses 4299 glycans, derived from the combined built-in resources of pGlyco-N-HighMannose.gdb and pGlyco-N-Human-multi.gdb of pGlyco3.…”

Section: Deepgp Enhances the Identification Sensitivity Of Glycopeptidesmentioning

confidence: 99%

Deep Learning Prediction of Glycopeptide Tandem Mass Spectra Powers Glycoproteomics

Zong,

Wang,

Qiu

et al. 2024

Preprint

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Protein glycosylation plays a significant role in numerous physiological and pathological cellular functions. Glycoproteomics based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) studies the protein glycosylation on a proteome-wide scale to get combinational information on glycosylation site, glycosylation level and glycan structure. However, the current sequence searching-based methods for glycoproteomics often fall short in glycan structure determination due to the limited occurrence of structure-determining ions. While spectral searching methods can utilize fragment intensity information to facilitate the identification of glycopeptides, its application is hindered by the difficulties in spectral library construction. In this work, we present DeepGP, a hybrid deep learning framework based on Transformer and graph neural network (GNN), for the prediction of MS/MS spectra and retention time of glycopeptides. Two GNN modules are utilized to capture the branched glycan structure and predict glycan ions intensity, respectively. Additionally, a pre-training strategy is implemented to alleviate the insufficiency of glycoproteomics data. Testing on multiple biological datasets, we demonstrate that DeepGP can predict MS/MS spectra and retention time of glycopeptides closely aligning with the experimental results. Comprehensive benchmarking of DeepGP on synthetic and biological datasets validates its effectiveness in distinguishing similar glycoforms. Remarkably, DeepGP can differentiate isomeric glycopeptides using MS/MS spectra without diagnostic ions. Based on various decoy methods, we demonstrated that DeepGP in combination with database searching can significantly increase the detection sensitivity of glycopeptides. We outlook that DeepGP can inspire extensive future work in glycoproteomics.

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

Cited by 10 publications

References 50 publications

Immunoglobulin A Carries SulfatedN-glycans Primarily at the Tailpiece Site – An Oxonium-Ion-Guided Approach for Site-SpecificN-glycan Identification

Immunoglobulin A Carries SulfatedN-glycans Primarily at the Tailpiece Site – An Oxonium-Ion-Guided Approach for Site-SpecificN-glycan Identification

Prediction of glycopeptide fragment mass spectra by deep learning

Deep Learning Prediction of Glycopeptide Tandem Mass Spectra Powers Glycoproteomics

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