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MSFragger-DDA+ enhances peptide identification sensitivity with full isolation window search

Author

Listed:
  • Fengchao Yu

    (University of Michigan)

  • Yamei Deng

    (University of Michigan)

  • Alexey I. Nesvizhskii

    (University of Michigan
    University of Michigan)

Abstract

Liquid chromatography-mass spectrometry based proteomics, particularly in the bottom-up approach, relies on the digestion of proteins into peptides for subsequent separation and analysis. The most prevalent method for identifying peptides from data-dependent acquisition mass spectrometry data is database search. Traditional tools typically focus on identifying a single peptide per tandem mass spectrum, often neglecting the frequent occurrence of peptide co-fragmentations leading to chimeric spectra. Here, we introduce MSFragger-DDA+, a database search algorithm that enhances peptide identification by detecting co-fragmented peptides with high sensitivity and speed. Utilizing MSFragger’s fragment ion indexing algorithm, MSFragger-DDA+ performs a comprehensive search within the full isolation window for each tandem mass spectrum, followed by robust feature detection, filtering, and rescoring procedures to refine search results. Evaluation against established tools across diverse datasets demonstrated that, integrated within the FragPipe computational platform, MSFragger-DDA+ significantly increases identification sensitivity while maintaining stringent false discovery rate control. It is also uniquely suited for wide-window acquisition data. MSFragger-DDA+ provides an efficient and accurate solution for peptide identification, enhancing the detection of low-abundance co-fragmented peptides. Coupled with the FragPipe platform, MSFragger-DDA+ enables more comprehensive and accurate analysis of proteomics data.

Suggested Citation

  • Fengchao Yu & Yamei Deng & Alexey I. Nesvizhskii, 2025. "MSFragger-DDA+ enhances peptide identification sensitivity with full isolation window search," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58728-z
    DOI: 10.1038/s41467-025-58728-z
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    References listed on IDEAS

    as
    1. Sangtae Kim & Pavel A. Pevzner, 2014. "MS-GF+ makes progress towards a universal database search tool for proteomics," Nature Communications, Nature, vol. 5(1), pages 1-10, December.
    2. Brian C. Searle & Lindsay K. Pino & Jarrett D. Egertson & Ying S. Ting & Robert T. Lawrence & Brendan X. MacLean & Judit Villén & Michael J. MacCoss, 2018. "Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    3. Fengchao Yu & Guo Ci Teo & Andy T. Kong & Klemens Fröhlich & Ginny Xiaohe Li & Vadim Demichev & Alexey I. Nesvizhskii, 2023. "Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. Manuel Matzinger & Anna Schmücker & Ramesh Yelagandula & Karel Stejskal & Gabriela Krššáková & Frédéric Berger & Karl Mechtler & Rupert L. Mayer, 2024. "Micropillar arrays, wide window acquisition and AI-based data analysis improve comprehensiveness in multiple proteomic applications," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Kevin L. Yang & Fengchao Yu & Guo Ci Teo & Kai Li & Vadim Demichev & Markus Ralser & Alexey I. Nesvizhskii, 2023. "MSBooster: improving peptide identification rates using deep learning-based features," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
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