Sarah Szwarc, Adriano Rutz, Kyungha Lee, Yassine Mejri, Olivier Bonnet, Hazrina Hazni, Adrien Jagora, Rany B. Mbeng Obame, Jin Kyoung Noh, Elvis Otogo N’Nang, Stephenie C. Alaribe, Khalijah Awang, Guillaume Bernadat, Young Hae Choi, Vincent Courdavault, Michel Frederich, Thomas Gaslonde, Florian Huber, Toh-Seok Kam, Yun Yee Low, Erwan Poupon, Justin J. J. van der Hooft, Kyo Bin Kang, Pierre Le Pogam, Mehdi A. Beniddir
- With over 3000 representatives, the monoterpene indole alkaloids (MIAs) class is among the most diverse families of plant natural products. The MS/MS spectral space exploration of these complex compounds using chemoinformatic and computational mass spectrometry tools offers a valuable opportunity to extract and share chemical insights from this emblematic family of natural products (NPs). In this work, we first present a substantially updated version of the MIADB, a database now containing 422 MS/MS spectra of MIAs that has been uploaded to the GNPS library versus 172 initial entries. We then introduce an innovative workflow that leverages hundreds of fragmentation spectra to support the FAIRification, extraction and dissemination of chemical knowledge. This workflow aims at the extraction of spectral patterns matching finely defined MIA skeletons. These extracted signatures can then be queried against complex biological extract datasets using MassQL. By applying this strategy to anWith over 3000 representatives, the monoterpene indole alkaloids (MIAs) class is among the most diverse families of plant natural products. The MS/MS spectral space exploration of these complex compounds using chemoinformatic and computational mass spectrometry tools offers a valuable opportunity to extract and share chemical insights from this emblematic family of natural products (NPs). In this work, we first present a substantially updated version of the MIADB, a database now containing 422 MS/MS spectra of MIAs that has been uploaded to the GNPS library versus 172 initial entries. We then introduce an innovative workflow that leverages hundreds of fragmentation spectra to support the FAIRification, extraction and dissemination of chemical knowledge. This workflow aims at the extraction of spectral patterns matching finely defined MIA skeletons. These extracted signatures can then be queried against complex biological extract datasets using MassQL. By applying this strategy to an LC-MS/MS dataset of 75 plant extracts, our results demonstrated the efficiency of this approach in identifying the diversity of MIA skeletons present in the analyzed samples. Additionally, our work enabled the digitization of structural data for diverse MIA skeletons by converting them into machine-readable formats and thereby enhancing their dissemination for the scientific community.
Scientific contribution
A comprehensive investigation of the monoterpene indole alkaloid chemical space, aiming to highlight skeleton-dependent fragmentation similarity trends and to generate valuable spectrometric signatures that could be used as queries.…


Metadaten| Author: | Sarah Szwarc, Adriano RutzORCiD, Kyungha Lee, Yassine Mejri, Olivier BonnetORCiD, Hazrina HazniORCiD, Adrien Jagora, Rany B. Mbeng Obame, Jin Kyoung Noh, Elvis Otogo N’NangORCiD, Stephenie C. AlaribeORCiD, Khalijah AwangORCiD, Guillaume Bernadat, Young Hae ChoiORCiD, Vincent CourdavaultORCiD, Michel FrederichORCiD, Thomas GaslondeORCiD, Florian HuberORCiDGND, Toh-Seok KamORCiD, Yun Yee LowORCiD, Erwan PouponORCiD, Justin J. J. van der HooftORCiD, Kyo Bin KangORCiD, Pierre Le PogamORCiD, Mehdi A. BeniddirORCiD |
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| Qualitätssicherung: | peer reviewed |
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| open access: | Gold - Erstveröffentlichung mit Lizenzhinweis |
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| Institutes: | Zentrum für Digitalisierung und Digitalität (ZDD) |
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| Fachbereich/Einrichtung: | Hochschule Düsseldorf / Fachbereich - Medien |
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| Document Type: | Article |
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| Year of Completion: | 2025 |
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| Language of Publication: | English |
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| Publisher: | Springer Nature |
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| Parent Title (English): | Journal of Cheminformatics |
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| Volume: | 17 |
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| Issue: | 1 |
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| Article Number: | 62 |
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| Page Number: | 15 |
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| URN: | urn:nbn:de:hbz:due62-opus-54607 |
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| DOI: | https://doi.org/10.1186/s13321-025-01009-0 |
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| ISSN: | 1758-2946 |
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| GND Keyword: | Computational chemistryGND; MassenspektrometrieGND; NaturstoffchemieGND |
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| Information on the Research Data: | All .d (Agilent), .mzXML, and .mgf datafiles in positive mode along with metadata and metabolite annotation tables of the 75 plant extracts dataset are available on the MassIVE repository under accession number MSV000096137, and with the following: https:// doi.org/10.25345/ C56Q1SV35. The 75 plant extracts molecular network job is accessible via this link: https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=ec37fe7e20764d38924c881ab9dd006e. Taxonomically Informed Metabolite Annotation outputs of the 75 plant extracts are accessible here: https://zenodo.org/records/14148771. All supporting data and materials related to the MIADB submission on the GNPS are available as follows: GNPS library link to the MIADB: https://gnps.ucsd.edu/ProteoSAFe/gnpslibrary.jsp?library=MIADB. All data used as input for heatmaps, dendrogram and pie charts are accessible here: https://github.com/spectra‑to‑ knowledge/Data‑processing‑and‑visualization/tree/main/Data. All codes used for data processing and visualization are available in the following GitHub repository: https://github.com/spectra-to-knowledge/miadb-visualization/tree/main/Data. All resulting files (outputs) of the previous processing and visualization steps are included in this GitHub repository: https://github.com/spectra-to-knowledge/miadb-visualization/tree/main/Outputs. All tables depicted in Supplementary Material are accessible in .tsv formats via the following link: https://github.com/spectra-to-knowledge/miadb-supplementaries/tree/main. |
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| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 005 Computerprogrammierung, Programme, Daten |
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| Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |
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| Release Date: | 2025/07/01 |
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