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Predicting glycan structure from tandem mass spectrometry via deep learning
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Abstract
Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 500,000 annotated MS/MS spectra, here we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw liquid chromatography–MS/MS data in seconds (top-1 accuracy: 90.3%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb . We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.
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Metadata
- Authors
- Urban, J., Jin, C., Thomsson, K. A., Karlsson, N. G., Ives, C. M., Fadda, E., & Bojar, D.
- Affiliations
- Wallenberg Centre for Molecular and Translational Medicine (SE)
- University of Gothenburg (SE)
- OsloMet – Oslo Metropolitan University (NO)
- National University of Ireland, Maynooth (IE)
- University of Southampton (GB)
- Corresponding author
- Bojar, Daniel
- Venue
- Nature Methods
- Published
- 2024-07
- Volume
- 21
- Issue
- 7
- Pages
- 1206-1215
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC11239490
- OpenAlex
- W4400208210
- Cited by
- 79
- References
- 58
- Citations by year
- 2026: 36 · 2025: 26 · 2024: 14
- FWCI
- 10.35
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion, other-oa)
- Funders
- Knut och Alice Wallenbergs Stiftelse
- Vetenskapsrådet (BioMS)
- Science Foundation Ireland (20/FFP-P/8809)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Glycosylation and Glycoproteins Research
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- glycan structure prediction, tandem mass spectrometry, deep learning, glycomics, fragment identification, post-translational modification
- MeSH terms
- Deep Learning, Chromatography, Liquid, Humans, Polysaccharides, Software, Neural Networks, Computer, Tandem Mass Spectrometry, Glycomics, Workflow
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