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<p>Bottom-up proteomics relies predominantly on collision-induced dissociation (CID) for peptide sequencing, which has achieved remarkable sensitivity and efficiency now enabling single-cell analysis. However, CID shows limitations in characterizing post-translational modifications and complex proteoforms. Here we have developed an integrated mass spectrometry platform enabling automated collision-, electron- and photon-based fragmentation techniques. Using multi-enzyme deep proteomics workflows, we generated comprehensive datasets to train a unified Prosit deep learning model predicting spectra across all dissociation methods. This publicly available model, now integrated into FragPipe’s MSBooster module, increased protein identifications by &gt;10% on average for both data-dependent and data-independent acquisition across all fragmentation techniques. We demonstrate that alternative approaches, particularly electron-induced and ultraviolet photodissociation, which generate richer, more informative spectra, achieve identification efficiency competitive with CID while providing superior sequence coverage. This work establishes a framework enabling routine application of advanced fragmentation techniques in standard proteomics pipelines.</p>

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Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月23日

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  • Nikita Levin,
  • Cemil Can Saylan,
  • Joel Lapin,
  • Yana Demyanenko,
  • Kevin L. Yang,
  • John Sidda,
  • Alexey I. Nesvizhskii,
  • Mathias Wilhelm,
  • Shabaz Mohammed

摘要

Bottom-up proteomics relies predominantly on collision-induced dissociation (CID) for peptide sequencing, which has achieved remarkable sensitivity and efficiency now enabling single-cell analysis. However, CID shows limitations in characterizing post-translational modifications and complex proteoforms. Here we have developed an integrated mass spectrometry platform enabling automated collision-, electron- and photon-based fragmentation techniques. Using multi-enzyme deep proteomics workflows, we generated comprehensive datasets to train a unified Prosit deep learning model predicting spectra across all dissociation methods. This publicly available model, now integrated into FragPipe’s MSBooster module, increased protein identifications by >10% on average for both data-dependent and data-independent acquisition across all fragmentation techniques. We demonstrate that alternative approaches, particularly electron-induced and ultraviolet photodissociation, which generate richer, more informative spectra, achieve identification efficiency competitive with CID while providing superior sequence coverage. This work establishes a framework enabling routine application of advanced fragmentation techniques in standard proteomics pipelines.