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Generating high quality libraries for DIA MS with empirically corrected peptide predictions

Author

Listed:
  • Brian C. Searle

    (Institute for Systems Biology
    Proteome Software, Inc.)

  • Kristian E. Swearingen

    (Institute for Systems Biology)

  • Christopher A. Barnes

    (Novo Nordisk Research Center Seattle, Inc.)

  • Tobias Schmidt

    (Technical University of Munich)

  • Siegfried Gessulat

    (Technical University of Munich
    SAP SE)

  • Bernhard Küster

    (Technical University of Munich
    Bavarian Center for Biomolecular Mass Spectrometry)

  • Mathias Wilhelm

    (Technical University of Munich)

Abstract

Data-independent acquisition approaches typically rely on experiment-specific spectrum libraries, requiring offline fractionation and tens to hundreds of injections. We demonstrate a library generation workflow that leverages fragmentation and retention time prediction to build libraries containing every peptide in a proteome, and then refines those libraries with empirical data. Our method specifically enables rapid, experiment-specific library generation for non-model organisms, which we demonstrate using the malaria parasite Plasmodium falciparum, and non-canonical databases, which we show by detecting missense variants in HeLa.

Suggested Citation

  • Brian C. Searle & Kristian E. Swearingen & Christopher A. Barnes & Tobias Schmidt & Siegfried Gessulat & Bernhard Küster & Mathias Wilhelm, 2020. "Generating high quality libraries for DIA MS with empirically corrected peptide predictions," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15346-1
    DOI: 10.1038/s41467-020-15346-1
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    Cited by:

    1. 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.
    2. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Martin Rodriguez & Brady Trevisan & Ritu M. Ramamurthy & Sunil K. George & Jonathan Diaz & Jordan Alexander & Diane Meares & Denise J. Schwahn & David R. Quilici & Jorge Figueroa & Michael Gautreaux &, 2023. "Transplanting FVIII/ET3-secreting cells in fetal sheep increases FVIII levels long-term without inducing immunity or toxicity," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Klemens Fröhlich & Eva Brombacher & Matthias Fahrner & Daniel Vogele & Lucas Kook & Niko Pinter & Peter Bronsert & Sylvia Timme-Bronsert & Alexander Schmidt & Katja Bärenfaller & Clemens Kreutz & Oliv, 2022. "Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Valdemaras Petrosius & Pedro Aragon-Fernandez & Nil Üresin & Gergo Kovacs & Teeradon Phlairaharn & Benjamin Furtwängler & Jeff Op De Beeck & Sarah L. Skovbakke & Steffen Goletz & Simon Francis Thomsen, 2023. "Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Charlotte Adams & Wassim Gabriel & Kris Laukens & Mario Picciani & Mathias Wilhelm & Wout Bittremieux & Kurt Boonen, 2024. "Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    7. Ronghui Lou & Weizhen Liu & Rongjie Li & Shanshan Li & Xuming He & Wenqing Shui, 2021. "DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation," Nature Communications, Nature, vol. 12(1), pages 1-15, December.

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