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Annotation of natural product compound families using molecular networking topology and structural similarity fingerprinting

Author

Listed:
  • Nicholas J. Morehouse

    (University of New Brunswick)

  • Trevor N. Clark

    (Simon Fraser University)

  • Emily J. McMann

    (Simon Fraser University)

  • Jeffrey A. Santen

    (Simon Fraser University)

  • F. P. Jake Haeckl

    (Simon Fraser University)

  • Christopher A. Gray

    (University of New Brunswick
    University of New Brunswick)

  • Roger G. Linington

    (Simon Fraser University)

Abstract

Spectral matching of MS2 fragmentation spectra has become a popular method for characterizing natural products libraries but identification remains challenging due to differences in MS2 fragmentation properties between instruments and the low coverage of current spectral reference libraries. To address this bottleneck we present Structural similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS) which matches chemical similarity grouping in the Natural Products Atlas to grouping of mass spectrometry features from molecular networking. This approach assigns compound families to molecular networking subnetworks without the need for experimental or calculated reference spectra. We demonstrate SNAP-MS can accurately annotate subnetworks built from both reference spectra and an in-house microbial extract library, and correctly predict compound families from published molecular networks acquired on a range of MS instrumentation. Compound family annotations for the microbial extract library are validated by co-injection of standards or isolation and spectroscopic analysis. SNAP-MS is freely available at www.npatlas.org/discover/snapms .

Suggested Citation

  • Nicholas J. Morehouse & Trevor N. Clark & Emily J. McMann & Jeffrey A. Santen & F. P. Jake Haeckl & Christopher A. Gray & Roger G. Linington, 2023. "Annotation of natural product compound families using molecular networking topology and structural similarity fingerprinting," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35734-z
    DOI: 10.1038/s41467-022-35734-z
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    References listed on IDEAS

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    1. Florian Huber & Lars Ridder & Stefan Verhoeven & Jurriaan H Spaaks & Faruk Diblen & Simon Rogers & Justin J J van der Hooft, 2021. "Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-18, February.
    2. Robin Schmid & Daniel Petras & Louis-Félix Nothias & Mingxun Wang & Allegra T. Aron & Annika Jagels & Hiroshi Tsugawa & Johannes Rainer & Mar Garcia-Aloy & Kai Dührkop & Ansgar Korf & Tomáš Pluskal & , 2021. "Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    3. Hosein Mohimani & Alexey Gurevich & Alexander Shlemov & Alla Mikheenko & Anton Korobeynikov & Liu Cao & Egor Shcherbin & Louis-Felix Nothias & Pieter C. Dorrestein & Pavel A. Pevzner, 2018. "Dereplication of microbial metabolites through database search of mass spectra," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
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