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Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data

Author

Listed:
  • Erik Hartman

    (Lund University)

  • Fredrik Forsberg

    (Lund University)

  • Sven Kjellström

    (Lund University)

  • Jitka Petrlova

    (Lund University)

  • Congyu Luo

    (Lund University)

  • Aaron Scott

    (Lund University)

  • Manoj Puthia

    (Lund University)

  • Johan Malmström

    (Lund University)

  • Artur Schmidtchen

    (Lund University)

Abstract

Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.

Suggested Citation

  • Erik Hartman & Fredrik Forsberg & Sven Kjellström & Jitka Petrlova & Congyu Luo & Aaron Scott & Manoj Puthia & Johan Malmström & Artur Schmidtchen, 2024. "Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51589-y
    DOI: 10.1038/s41467-024-51589-y
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    References listed on IDEAS

    as
    1. Christian T. Madsen & Jan C. Refsgaard & Felix G. Teufel & Sonny K. Kjærulff & Zhe Wang & Guangjun Meng & Carsten Jessen & Petteri Heljo & Qunfeng Jiang & Xin Zhao & Bo Wu & Xueping Zhou & Yang Tang &, 2022. "Combining mass spectrometry and machine learning to discover bioactive peptides," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Michael Zasloff, 2002. "Antimicrobial peptides of multicellular organisms," Nature, Nature, vol. 415(6870), pages 389-395, January.
    3. Erik Hartman & Aaron M. Scott & Christofer Karlsson & Tirthankar Mohanty & Suvi T. Vaara & Adam Linder & Lars Malmström & Johan Malmström, 2023. "Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
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