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<p>Deep learning has shown great promise for high-throughput spectral library construction in glycoproteomics. Nevertheless, precise prediction of the structural spectra of intact N-glycopeptides remains challenging due to their complex and high-dimensional information. Here we present SpecGP, a transformer-based architecture featuring an attention-enhanced glycan fragment encoding strategy with multilayer perceptrons for accurate glycopeptide spectrum prediction. The model improves spectral differentiation among glycopeptides by expanding fragment ion coverage while retaining high prediction accuracy. By predicting mass spectra at multiple collision energies, SpecGP maximizes the detection of key diagnostic ions and ensures broad compatibility with diverse experimental datasets. It also improves retention time prediction by using a dual-task framework. In applications, SpecGP effectively enhances isomeric discrimination by incorporating a self-supervised weighting training strategy and boosts glycopeptide identification via rescoring. The capability of glycan structure discrimination can be further strengthened and validated by complementary diagnostic ions with dynamic intensities in multi-energy spectra.</p>

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SpecGP as a transformer-based model for predicting energy-adaptable structural spectra of glycopeptides

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Xianyong Wang,
  • Rui Song,
  • Zhuangzhuang Feng,
  • Shisheng Sun

摘要

Deep learning has shown great promise for high-throughput spectral library construction in glycoproteomics. Nevertheless, precise prediction of the structural spectra of intact N-glycopeptides remains challenging due to their complex and high-dimensional information. Here we present SpecGP, a transformer-based architecture featuring an attention-enhanced glycan fragment encoding strategy with multilayer perceptrons for accurate glycopeptide spectrum prediction. The model improves spectral differentiation among glycopeptides by expanding fragment ion coverage while retaining high prediction accuracy. By predicting mass spectra at multiple collision energies, SpecGP maximizes the detection of key diagnostic ions and ensures broad compatibility with diverse experimental datasets. It also improves retention time prediction by using a dual-task framework. In applications, SpecGP effectively enhances isomeric discrimination by incorporating a self-supervised weighting training strategy and boosts glycopeptide identification via rescoring. The capability of glycan structure discrimination can be further strengthened and validated by complementary diagnostic ions with dynamic intensities in multi-energy spectra.