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- Title
Prediction of glycopeptide fragment mass spectra by deep learning.
- Authors
Yang, Yi; Fang, Qun
- 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. Deep learning has achieved a notable success in proteomics and is now emerging in glycoproteomics. Here, the authors develop a neural network-based method to predict mass spectra of intact glycopeptides and demonstrate its potential in data-dependent and data-independent acquisition glycoproteomics.
- Subjects
DEEP learning; MASS spectrometry; GRAPH neural networks; GLYCAN structure; STRUCTURAL isomers; GLYCANS; GLYCOPEPTIDES
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-46771-1