MS2Query: reliable and scalable MS2 mass spectra-based analogue search

  • Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offersMetabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology.show moreshow less

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Author:Niek F. de JongeORCiD, Joris J. R. Louwen, Elena ChekmenevaORCiD, Stephane CamuzeauxORCiD, Femke J. Vermeir, Robert S. JansenORCiD, Florian HuberORCiD, Justin J. J. van der HooftORCiD
Qualitätssicherung:peer reviewed
open access:Gold - Erstveröffentlichung mit Lizenzhinweis
Fachbereich/Einrichtung:Hochschule Düsseldorf / Fachbereich - Medien
Document Type:Article
Year of Completion:2023
Language of Publication:English
Publisher:Springer
Parent Title (English):Nature Communications
Volume:14
Article Number:1752
Page Number:12
URN:urn:nbn:de:hbz:due62-opus-46105
DOI:https://doi.org/10.1038/s41467-023-37446-4
ISSN:2041-1723
Tag:Complex Mixtures; Machine Learning; Mass Spectrometry; Metabolomics
GND Keyword:Metabolomik; Massenspektrometrie; Maschinelles Lernen; Reproduzierbarkeit
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2024/10/01
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