Gaudry, A. | Search bioRxiv

bioRxiv2022

Inventa: a computational tool to discover chemical novelty in natural extracts libraries

Collections of natural extracts hold potential for the discovery of novel natural products with original modes of action. The prioritization of extracts from collections remains challenging due to the lack of workflow that combines multiple-source information to facilitate the data interpretation. Results from different analysis techniques and literature reports need to be organized, processed, and interpreted to enable optimal decision-making for extracts prioritization. Here, we introduce Inventa, a computational tool that highlights the chemical novelty potential within extracts, considering untargeted mass spectrometry data, spectral annotation, and literature reports. Based on this information, Inventa calculates multiple scores that inform their chemical potential. Thus, Inventa has the potential to accelerate new natural products discovery. Inventa was applied to a set of plants from the Celastraceae family as a proof of concept. The Pristimera indica (Willd.) A.C.Sm roots extract was highlighted as a promising source of potentially novel compounds. Its phytochemical investigation resulted in the isolation and de novo characterization of thirteen new dihydro-{beta}-agarofuran sesquiterpenes, five of them presenting a new 9-oxodihydro-{beta}-agarofuran base scaffold.

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bioRxiv2021

MEMO: Mass Spectrometry-based Sample Vectorization to Explore Chemodiverse Datasets

In natural products research, chemodiverse extracts coming from multiple organisms are explored for novel bioactive molecules, sometimes over extended periods. Samples are usually analyzed by liquid chromatography coupled with fragmentation mass spectrometry to acquire informative mass spectral ensembles. Such data is then exploited to establish relationships among analytes or samples (e.g. via molecular networking) and annotate metabolites. However, the comparison of samples profiled in different batches is challenging with current metabolomics methods. Indeed, the experimental variation - changes in chromatographical or mass spectrometric conditions - often hinders the direct comparison of the profiled samples. Here we introduce MEMO - MS2 BasEd SaMple VectOrization - a method allowing to cluster large amounts of chemodiverse samples based on their LC-MS/MS profiles in a retention time agnostic manner. This method is particularly suited for heterogeneous and chemodiverse sample sets. MEMO demonstrated similar clustering performance as state-of-the-art metrics taking into account fragmentation spectra. More importantly, such performance was achieved without the requirement of a prior feature alignment step and in a significantly shorter computational time. MEMO thus allows the comparison of vast ensembles of samples, even when analyzed over long periods of time, and on different chromatographic or mass spectrometry platforms. This new addition to the computational metabolomics toolbox should drastically expand the scope of large-scale comparative analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/474089v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@c540c0org.highwire.dtl.DTLVardef@1e07e7org.highwire.dtl.DTLVardef@198e030org.highwire.dtl.DTLVardef@1a6a15e_HPS_FORMAT_FIGEXP M_FIG C_FIG

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