Scholarlink

<p>Deep learning has advanced mass spectrometry data interpretation, yet most models remain feature extractors rather than unified scoring frameworks. We present pUniFind, a large-scale multimodal foundational model in proteomics that integrates open end-to-end peptide-spectrum scoring with open, zero-shot de novo sequencing. Trained on over 100 million open search-derived spectra, pUniFind aligns spectral and peptide modalities through cross-modality prediction alongside other carefully designed pretraining tasks. Consequently, further benefiting from its open scoring capability, pUniFind outperforms traditional engines across diverse datasets, notably achieving a 42.6% increase in identified peptides in immunopeptidomics. We propose two de novo sequencing workflows to support different applications. For modification-rich de novo sequencing, pUniFind identifies 60% more peptide–spectrum matches than existing de novo methods despite a 300 times larger search space. For regular de novo sequencing, pUniFind recovers an additional 38.5% of peptides, including 1,891 that map to the genome but are absent from reference proteomes. Crucially, it achieves this while preserving full fragment ion coverage and maintaining high consistency with database-search-based methods. Furthermore, a quality control module based on deep learning-derived features increases the consistency of results with RNA-Seq evidence from 65.4% to 85.0%. These results establish a unified, scalable deep learning framework for proteomic analysis, offering improved sensitivity, modification coverage, and interpretability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A large-scale unified deep learning model for peptide mass spectrum interpretation trained on multimodal data

  • 期刊论文

  • 发表日期：

    2026年5月25日

查看全文
  • Jiale Zhao,
  • Pengzhi Mao,
  • Kaifei Wang,
  • Yiming Li,
  • Yaping Peng,
  • Ranfei Chen,
  • Shuqi Lu,
  • Xiaohong Ji,
  • Jiaxiang Ding,
  • Xin Zhang,
  • Yucheng Liao,
  • Weinan E,
  • Han Wen,
  • Weijie Zhang,
  • Hao Chi

摘要

Deep learning has advanced mass spectrometry data interpretation, yet most models remain feature extractors rather than unified scoring frameworks. We present pUniFind, a large-scale multimodal foundational model in proteomics that integrates open end-to-end peptide-spectrum scoring with open, zero-shot de novo sequencing. Trained on over 100 million open search-derived spectra, pUniFind aligns spectral and peptide modalities through cross-modality prediction alongside other carefully designed pretraining tasks. Consequently, further benefiting from its open scoring capability, pUniFind outperforms traditional engines across diverse datasets, notably achieving a 42.6% increase in identified peptides in immunopeptidomics. We propose two de novo sequencing workflows to support different applications. For modification-rich de novo sequencing, pUniFind identifies 60% more peptide–spectrum matches than existing de novo methods despite a 300 times larger search space. For regular de novo sequencing, pUniFind recovers an additional 38.5% of peptides, including 1,891 that map to the genome but are absent from reference proteomes. Crucially, it achieves this while preserving full fragment ion coverage and maintaining high consistency with database-search-based methods. Furthermore, a quality control module based on deep learning-derived features increases the consistency of results with RNA-Seq evidence from 65.4% to 85.0%. These results establish a unified, scalable deep learning framework for proteomic analysis, offering improved sensitivity, modification coverage, and interpretability.