INVENTA: a workflow for discovering chemical novelty in natural products extracts libraries

In Natural Product (NP) research the efficient prioritization of samples in extract libraries has become a key element for the discovery of original active specialized metabolites [1]. INVENTA is an automated untargeted mass spectrometry (MS) structure-based prioritization workflow that allows extract selection, based on the possibility of structural novelty of their metabolites, from libraries of biological samples analyzed by in-depth untargeted UHPLC-MS/MS metabolite profiling. To achieve this, spectral organization is performed through molecular networking and MS/MS spectra are annotated by a combination of advanced computational methods that yield molecular formula and chemical classes assessment as well as best candidate structure ranking. INVENTA integrates previous literature reports on the taxon through automated search in an online resource for NP structure occurrences in their source organisms (LOTUS) [2]. Furthermore, INVENTA uses the chemical distances between samples to assess the chemical diversity of a given sample within a library of profiled extracts [3]. Based on such data INVENTA provides combined scores that allow extracts prioritization based on chemical novelty and results can be complemented with bioactivity screening data for the identification of novel bioactive NPs. As a proof of concept, INVENTA was applied to a collection of taxonomically related samples of the Celastraceae family. The ethyl acetate extract of Pristemira indica roots was highlighted as a potential source of original metabolites. The phytochemical study resulted in the characterization of 13 new dihydro-β-agarofuran sesquiterpenes and illustrated how Inventa can speed up the discovery of novel natural products.

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• References

• 1 Wolfender JL, Litaudon M, Touboul D. et al. Innovative omics-based approaches for prioritisation and targeted isolation of natural products-new strategies for drug discovery. Nat Prod Rep 2019; 36: 855-868
• 2 Rutz A, Sorokina M, Galgonek J. et al. The LOTUS Initiative for Open Natural Products Research: Knowledge Management through Wikidata. Elife 2022; 11: e70780
• 3 Gaudry A, Huber F, Nothias L-F. et al. MEMO: Mass Spectrometry-Based Sample Vectorization to Explore Chemodiverse Datasets. Frontiers in Bioinformatics 2022; 2: 842964

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Artikel online veröffentlicht:
12. Dezember 2022