Literature DB >> 29498278

Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation.

Louis-Félix Nothias1,2,3, Mélissa Nothias-Esposito3,4, Ricardo da Silva1,2, Mingxun Wang1,2, Ivan Protsyuk5, Zheng Zhang1,2, Abi Sarvepalli1,2, Pieter Leyssen6, David Touboul3, Jean Costa4, Julien Paolini4, Theodore Alexandrov2,5, Marc Litaudon3, Pieter C Dorrestein1,2.   

Abstract

It is a common problem in natural product therapeutic lead discovery programs that despite good bioassay results in the initial extract, the active compound(s) may not be isolated during subsequent bioassay-guided purification. Herein, we present the concept of bioactive molecular networking to find candidate active molecules directly from fractionated bioactive extracts. By employing tandem mass spectrometry, it is possible to accelerate the dereplication of molecules using molecular networking prior to subsequent isolation of the compounds, and it is also possible to expose potentially bioactive molecules using bioactivity score prediction. Indeed, bioactivity score prediction can be calculated with the relative abundance of a molecule in fractions and the bioactivity level of each fraction. For that reason, we have developed a bioinformatic workflow able to map bioactivity score in molecular networks and applied it for discovery of antiviral compounds from a previously investigated extract of Euphorbia dendroides where the bioactive candidate molecules were not discovered following a classical bioassay-guided fractionation procedure. It can be expected that this approach will be implemented as a systematic strategy, not only in current and future bioactive lead discovery from natural extract collections but also for the reinvestigation of the untapped reservoir of bioactive analogues in previous bioassay-guided fractionation efforts.

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Year:  2018        PMID: 29498278     DOI: 10.1021/acs.jnatprod.7b00737

Source DB:  PubMed          Journal:  J Nat Prod        ISSN: 0163-3864            Impact factor:   4.050


  64 in total

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