ArticleNature communications2024
Prediction of glycopeptide fragment mass spectra by deep learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 34 citations in OpenAlex.
- Deep Learning Prediction of O-Glycopeptide Tandem Mass Spectra Enhances O-Glycoproteomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The 'sugar' side of extracellular vesicle-glycome: a panorama from basic characteristics, deciphering technologies, functions, to applications.Journal of nanobiotechnology · 2026Review
- Mass Spectrometry-Based Proteomics Methods for Systematic Identification and Quantification of Protein O-Glycosylation in Complex Biological Samples.Journal of the American Society for Mass Spectrometry · 2026Review
- The ProteomeXchange consortium in 2026: making proteomics data FAIR.Nucleic acids research · 2026Article
- Mass Spectrometry Analysis of Biotherapeutic Monoclonal Antibody Glycosylation.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- Article
- Ultradeep N-glycoproteome atlas of mouse reveals spatiotemporal signatures of brain aging and neurodegenerative diseases.Nature communications · 2025Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Intelligent rehabilitation in an aging population: empowering human-machine interaction for hand function rehabilitation through 3D deep learning and point cloud.Frontiers in computational neuroscience · 2025Article
- Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers.Expert review of proteomics · 2024Review
- Mass spectrometry-based structure-specificActa biochimica et biophysica Sinica · 2024Review
- Prediction of glycopeptide fragment mass spectra by deep learning.Nature communications · 2024Article
- Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain.Mass spectrometry reviewsReview
- Software Design and Analytical Challenges for Confident Glycopeptide Identification With Data-Independent Acquisition.Mass spectrometry reviewsReview
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
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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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.