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CANPA: Computer-Assisted Natural Products Anticipation

Authors :
Erwan Poupon
Pierre Champy
Coralie Pavesi
Vincent Dumontet
Grégory Genta-Jouve
Alexander E. Fox Ramos
Mehdi A. Beniddir
Marc Litaudon
Biomolécules : Conception, Isolement, Synthèse (BioCIS)
Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Université de Cergy Pontoise (UCP)
Université Paris-Seine-Université Paris-Seine
Université Paris-Saclay
Centre National de la Recherche Scientifique (CNRS)
Institut de Chimie des Substances Naturelles (ICSN)
Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)
Cibles Thérapeutiques et conception de médicaments (CiTCoM - UMR 8038)
Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)
Source :
Analytical Chemistry, Analytical Chemistry, American Chemical Society, 2019, 91 (17), pp.11247-11252. ⟨10.1021/acs.analchem.9b02216⟩
Publication Year :
2019
Publisher :
American Chemical Society (ACS), 2019.

Abstract

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 structure 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 able 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 non peptidic 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.

Details

ISSN :
15206882 and 00032700
Volume :
91
Database :
OpenAIRE
Journal :
Analytical Chemistry
Accession number :
edsair.doi.dedup.....0435c119db4cc6f82ed91c29a447980d
Full Text :
https://doi.org/10.1021/acs.analchem.9b02216