CANPA: Computer-Assisted Natural Products Anticipation

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Ramos, AEF
Pavesi, C
Litaudon, M
Dumontet, V
Poupon, E
Champy, P
Genta-Jouve, G
Beniddir, MA
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ACS Publications
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Traditional natural products discovery workflows implying a combination of different targeting strategies, including structure- and/or bioactivity-based approaches, afford no information about new compound structures until late in the discovery pipeline. By integrating a MS/MS prediction module and a collaborative library of (bio)chemical transformations, we have developed a new platform, coined MetWork, that is capable of anticipating the structural identity of metabolites starting from any identified compound. In our quest to discover new monoterpene indole alkaloids, we demonstrate the utility of the MetWork platform by anticipating the structures of five previously undescribed sarpagine-like N-oxide alkaloids that have been targeted and isolated from the leaves of Alstonia balansae using a molecular networking-based dereplication strategy fueled by computer-generated annotations. This study constitutes the first example of nonpeptidic molecular networking-based natural product discovery workflow, in which the targeted structures were initially generated, and therefore anticipated by a computer prior to their isolation.
This work has been developed with the funding from Cienciactiva, an initiative of the National Council for Science, Technology and Technological Innovation (CONCYTEC) contract 239-2015-FONDECYT. This was also supported by the French ANR grant (ANR-15-CE29-0001). We express our thanks to Jean-Christophe Jullian (BioCIS) for performing the NMR analysis of the isolated compounds. We also thank Karine Leblanc (BioCIS) for her assistance in the preparative HPLC purifications. We are very grateful to North Province of New Caledonia and Falconbridge New Caledonia that have facilitated our field investigation.
Palabras clave
structure elucidation, Chemical prospecting, Dereplication, Molecular Networking-Based Study, MetWork, Alkaloids, N-oxide, in silico metabolization