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ACS Chem Biol. 2017 Oct 20;12(10):2644-2651. doi: 10.1021/acschembio.7b00413. Epub 2017 Sep 15.

Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks.

Author information

1
Institut de Chimie des Substances Naturelles, CNRS-ICSN, UPR 2301, Université Paris-Saclay , 91198, Gif-sur-Yvette, France.
2
School of Pharmaceutical Sciences, University of Geneva, University of Lausanne , CMU - Rue Michel Servet 1, 1211 Geneva 11, Switzerland.
3
Department of Pharmacology and Toxicology, University of Lausanne , CH-1005 Lausanne, Switzerland.
4
Equipe C-TAC, UMR CNRS 8638 COMETE - Université Paris Descartes , 4 avenue de l'Observatoire, 75006 Paris, France.
5
Laboratory for Virology and Experimental Chemotherapy, Rega Institute for Medical Research , KU Leuven Minderbroedersstraat 10, B-3000 Leuven, Belgium.
6
Laboratoire de Pharmacognosie, UMR CNRS 8638 COMETE - Université Paris Descartes , 4 avenue de l'Observatoire, 75006 Paris, France.
7
School of Biomedicine, Far Eastern Federal University , Vladivostok, Russian Federation.

Abstract

Natural products represent an inexhaustible source of novel therapeutic agents. Their complex and constrained three-dimensional structures endow these molecules with exceptional biological properties, thereby giving them a major role in drug discovery programs. However, the search for new bioactive metabolites is hampered by the chemical complexity of the biological matrices in which they are found. The purification of single constituents from such matrices requires such a significant amount of work that it should be ideally performed only on molecules of high potential value (i.e., chemical novelty and biological activity). Recent bioinformatics approaches based on mass spectrometry metabolite profiling methods are beginning to address the complex task of compound identification within complex mixtures. However, in parallel to these developments, methods providing information on the bioactivity potential of natural products prior to their isolation are still lacking and are of key interest to target the isolation of valuable natural products only. In the present investigation, we propose an integrated analysis strategy for bioactive natural products prioritization. Our approach uses massive molecular networks embedding various informational layers (bioactivity and taxonomical data) to highlight potentially bioactive scaffolds within the chemical diversity of crude extracts collections. We exemplify this workflow by targeting the isolation of predicted active and nonactive metabolites from two botanical sources (Bocquillonia nervosa and Neoguillauminia cleopatra) against two biological targets (Wnt signaling pathway and chikungunya virus replication). Eventually, the detection and isolation processes of a daphnane diterpene orthoester and four 12-deoxyphorbols inhibiting the Wnt signaling pathway and exhibiting potent antiviral activities against the CHIKV virus are detailed. Combined with efficient metabolite annotation tools, this bioactive natural products prioritization pipeline proves to be efficient. Implementation of this approach in drug discovery programs based on natural extract screening should speed up and rationalize the isolation of bioactive natural products.

PMID:
28829118
DOI:
10.1021/acschembio.7b00413
[Indexed for MEDLINE]

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