Bagdonaite, I. et al. Glycoproteomics. Nat. Rev. Methods Primers 2, 48 (2022).
Bern, M., Kil, Y. J. & Becker, C. Byonic: advanced peptide and protein identification software. Curr. Protoc. Bioinform. 40, 13 (2012).
Zeng, W.-F., Cao, W.-Q., Liu, M.-Q., He, S.-M. & Yang, P.-Y. Precise, fast and comprehensive analysis of intact glycopeptides and modified glycans with pGlyco3. Nat. Methods 18, 1515–1523 (2021).
Polasky, D. A., Yu, F., Teo, G. C. & Nesvizhskii, A. I. Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco. Nat. Methods 17, 1125–1132 (2020).
Lu, L., Riley, N. M., Shortreed, M. R., Bertozzi, C. R. & Smith, L. M. O-pair search with MetaMorpheus for O-glycopeptide characterization. Nat. Methods 17, 1133–1138 (2020).
Fang, Z. et al. Glyco-Decipher enables glycan database-independent peptide matching and in-depth characterization of site-specific N-glycosylation. Nat. Commun. 13, 1900 (2022).
Xiao, K. & Tian, Z. GPSeeker enables quantitative structural N-glycoproteomics for site- and structure-specific characterization of differentially expressed N-glycosylation in hepatocellular carcinoma. J. Proteome Res. 18, 2885–2895 (2019).
Sun, W. et al. Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics. Nat. Commun. 14, 4046 (2023).
Shen, J. et al. StrucGP: de novo structural sequencing of site-specific N-glycan on glycoproteins using a modularization strategy. Nat. Methods 18, 921–929 (2021).
Toghi Eshghi, S., Shah, P., Yang, W., Li, X. & Zhang, H. GPQuest: a spectral library matching algorithm for site-specific assignment of tandem mass spectra to intact N-glycopeptides. Anal. Chem. 87, 5181–5188 (2015).
Li, S., Zhu, J., Lubman, D. M., Zhou, H. & Tang, H. GlycoSLASH: concurrent glycopeptide identification from multiple related LC-MS/MS data sets by using spectral clustering and library searching. J. Proteome Res. 22, 1501–1509 (2023).
Ye, Z., Mao, Y., Clausen, H. & Vakhrushev, S. Y. Glyco-DIA: a method for quantitative O-glycoproteomics with in silico-boosted glycopeptide libraries. Nat. Methods 16, 902–910 (2019).
Yang, Y. et al. GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control. Nat. Commun. 12, 6073 (2021).
Searle, B. C. et al. Generating high quality libraries for DIA MS with empirically corrected peptide predictions. Nat. Commun. 11, 1548 (2020).
Yang, Y. et al. In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics. Nat. Commun. 11, 146 (2020).
Demichev, V. et al. dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nat. Commun. 13, 3944 (2022).
Yu, Y. & Li, M. Towards highly sensitive deep learning-based end-to-end database search for tandem mass spectrometry. Nat. Mach. Intell. 7, 85–95 (2025).
Bouwmeester, R., Gabriels, R., Hulstaert, N., Martens, L. & Degroeve, S. DeepLC can predict
Here is the paraphrased version of the provided HTML content, with the text rewritten for clarity and readability while preserving the original structure and language:
Retention times for peptides carrying previously unidentified modifications. Nat. Methods 18, 1363–1369 (2021).
Google Scholar
Zeng, W.-F. et al. AlphaPeptDeep: a modular deep learning framework for predicting peptide properties in proteomics. Nat. Commun. 13, 7238 (2022).
Google Scholar
Tiwary, S. et al. Accurate MS/MS spectrum prediction for both data-dependent and data-independent acquisition workflows. Nat. Methods 16, 519–525 (2019).
Google Scholar
Tarn, C. & Zeng, W. F. pDeep3: enhanced spectrum prediction via rapid few-shot learning. Anal. Chem. 93, 5815–5822 (2021).
Google Scholar
Gessulat, S. et al. Prosit: deep learning–driven prediction of peptide tandem mass spectra across the proteome. Nat. Methods 16, 509–518 (2019).
Google Scholar
Zong, Y. et al. DeepFLR enables precise control of false localization rates in phosphoproteomics. Nat. Commun. 14, 2269 (2023).
Google Scholar
Li, K., Jain, A., Malovannaya, A., Wen, B. & Zhang, B. DeepRescore: using deep learning to enhance peptide identification in immunopeptidomics. Proteomics 20, e1900334 (2020).
Google Scholar
Yang, K. L. et al. MSBooster: boosting peptide identification rates with deep learning–derived features. Nat. Commun. 14, 4539 (2023).
Google Scholar
Zhou, W.-J., Wei, Z.-H., He, S.-M. & Chi, H. pValid 2: a deep learning–based method for validating peptide identifications in shotgun proteomics with improved discrimination. J. Proteom. 251, 104414 (2022).
Google Scholar
Wilhelm, M. et al. Deep learning enhances sensitivity in mass spectrometry–based immunopeptidomics. Nat. Commun. 12, 3346 (2021).
Google Scholar
Ma, C. et al. Advancing peptide retention time prediction in liquid chromatography via deep learning. Anal. Chem. 90, 10881–10888 (2018).
Google Scholar
Cox, J. Machine learning–based prediction of peptide mass spectral libraries. Nat. Biotechnol. 41, 33–43 (2023).
Google Scholar
Zong, Y., Wang, Y., Qiu, X., Huang, X. & Qiao, L. Deep learning–driven prediction of glycopeptide tandem mass spectra advances glycoproteomics. Nat. Mach. Intell. 6, 950–961 (2024).
Google Scholar
Yang, Y. & Fang, Q. Deep learning predicts glycopeptide fragment mass spectra. Nat. Commun. 15, 2448 (2024).
Google Scholar
Vaswani, A. et al. Attention is all you need. In Proc. Advances in Neural Information Processing Systems Vol. 30 (eds Guyon, I. et al.) (Curran Associates, 2017).
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M. & Monfardini, G. The graph neural network model. IEEE Trans. Neural Netw. 20, 61–80 (2009).
Google Scholar
Tai, K. S., Socher, R. & Manning, C. D. Improved semantic representations using tree-structured long short-term memory networks. Preprint at (2015).
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
Google Scholar
Zhang, S. Predicting spectra and retention times of N-glycopeptides with deep learning. MSc thesis, Univ. Waterloo (2023).
Klein, J., Carvalho, L. & Zaia, J. Expanding N-glycopeptide identification by modeling fragmentation, elution behavior, and the glycome.
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Liu, G. et al. Deep learning-based prediction of inter-residue connectivity. Nat. Commun. 15, 6168 (2024).
Zhang, Z. & Shah, B. Predicting collision-induced dissociation spectra of common N-glycopeptides to identify glycoforms. Anal. Chem. 82, 10194–10202 (2010).
Ying, C. et al. Do transformers perform poorly for graph representation? In Proc. Advances in Neural Information Processing Systems Vol. 34 (eds Ranzato, M. et al.) 28877–28888 (Curran Associates, 2021).
Young, A., Röst, H. & Wang, B. Predicting tandem mass spectra of small molecules using graph transformers. Nat. Mach. Intell. 6, 404–416 (2024).
Dang, L. et al. Identifying bisecting N-glycans on intact glycopeptides using two characteristic ions in tandem mass spectra. Anal. Chem. 91, 5478–5482 (2019).
Liu, M.-Q. et al. pGlyco 2.0 enables precise N-glycoproteomics with thorough quality control and one-step mass spectrometry for intact glycopeptide identification. Nat. Commun. 8, 438 (2017).
Chen, Z. et al. Detecting core-fucosylated glycopeptides using the Y1+Fuc/Y1 ratio in low-energy HCD spectra. Anal. Chem. 94, 17349–17353 (2022).
Xin, M. et al. Precision glycoproteomics reveals unique N-glycosylation patterns in human sperm cells. Mol. Cell. Proteomics 21, 100214 (2022).
Xin, M. et al. Detailed structural analysis of site-specific N-glycans in seminal plasma. J. Proteome Res. 21, 1664–1674 (2022).
Abramson, J. et al. Accurate prediction of biomolecular interaction structures with AlphaFold 3. Nature 630, 493–500 (2024).
Bekker-Jensen, D. B. et al. A refined shotgun approach for quickly generating comprehensive human proteomes. Cell Syst. 4, 587–599 (2017).
Shen, J. & Sun, S. StrucGP: software for interpreting N-glycan structures on intact glycopeptides using tandem mass spectrometry data. Zenodo (2021).
The, M., MacCoss, M. J., Noble, W. S. & Käll, L. Fast and reliable protein false discovery rate estimation in large-scale proteomics datasets using Percolator 3.0. J. Am. Soc. Mass Spectrom. 27, 1719–1727 (2016).
Chen, T. et al. iProX in 2021: linking proteomics data sharing with big data. Nucleic Acids Res. 50, D1522–D1527 (2022).
Wang, X., Song, R., Feng, Z. & Sun, S. SpecGP: a transformer-based model for predicting energy-adaptable structural spectra of glycopeptides. Code Ocean (2026).
Wang, X., Song, R., Feng, Z. & Sun, S. SpecGP: a transformer-based model for predicting energy-adaptable structural spectra of glycopeptides. Zenodo (2026).



