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Deep learning the collisional cross sections of the peptide universe from a million experimental values

Author

Listed:
  • Florian Meier

    (Max Planck Institute of Biochemistry
    Jena University Hospital)

  • Niklas D. Köhler

    (Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health)

  • Andreas-David Brunner

    (Max Planck Institute of Biochemistry)

  • Jean-Marc H. Wanka

    (Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health)

  • Eugenia Voytik

    (Max Planck Institute of Biochemistry)

  • Maximilian T. Strauss

    (Max Planck Institute of Biochemistry)

  • Fabian J. Theis

    (Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health
    TU München)

  • Matthias Mann

    (Max Planck Institute of Biochemistry
    University of Copenhagen)

Abstract

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

Suggested Citation

  • Florian Meier & Niklas D. Köhler & Andreas-David Brunner & Jean-Marc H. Wanka & Eugenia Voytik & Maximilian T. Strauss & Fabian J. Theis & Matthias Mann, 2021. "Deep learning the collisional cross sections of the peptide universe from a million experimental values," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21352-8
    DOI: 10.1038/s41467-021-21352-8
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    Cited by:

    1. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Alice Fletcher & Dean Clift & Emma Vries & Sergio Martinez Cuesta & Timothy Malcolm & Francesco Meghini & Raghothama Chaerkady & Junmin Wang & Abby Chiang & Shao Huan Samuel Weng & Jonathan Tart & Edm, 2023. "A TRIM21-based bioPROTAC highlights the therapeutic benefit of HuR degradation," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Naomi Hoenisch Gravel & Annika Nelde & Jens Bauer & Lena Mühlenbruch & Sarah M. Schroeder & Marian C. Neidert & Jonas Scheid & Steffen Lemke & Marissa L. Dubbelaar & Marcel Wacker & Anna Dengler & Rei, 2023. "TOFIMS mass spectrometry-based immunopeptidomics refines tumor antigen identification," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    4. Lei Xin & Rui Qiao & Xin Chen & Hieu Tran & Shengying Pan & Sahar Rabinoviz & Haibo Bian & Xianliang He & Brenton Morse & Baozhen Shan & Ming Li, 2022. "A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. Wen-Feng Zeng & Xie-Xuan Zhou & Sander Willems & Constantin Ammar & Maria Wahle & Isabell Bludau & Eugenia Voytik & Maximillian T. Strauss & Matthias Mann, 2022. "AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics," Nature Communications, Nature, vol. 13(1), pages 1-14, 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. 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.
    8. Weiping Sun & Qianqiu Zhang & Xiyue Zhang & Ngoc Hieu Tran & M. Ziaur Rahman & Zheng Chen & Chao Peng & Jun Ma & Ming Li & Lei Xin & Baozhen Shan, 2023. "Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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