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Bioactive Natural Products Prioritization Using Massive Multi-informational Molecular Networks
ACS Chemical Biology ( IF 4 ) Pub Date : 2017-09-15 00:00:00 , DOI: 10.1021/acschembio.7b00413
Florent Olivon 1 , Pierre-Marie Allard 2 , Alexey Koval 3 , Davide Righi 2 , Gregory Genta-Jouve 4 , Johan Neyts 5 , Cécile Apel 1 , Christophe Pannecouque 5 , Louis-Félix Nothias 1 , Xavier Cachet 6 , Laurence Marcourt 2 , Fanny Roussi 1 , Vladimir L. Katanaev 3, 7 , David Touboul 1 , Jean-Luc Wolfender 2 , Marc Litaudon 1
Affiliation  

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.

中文翻译:

使用大规模多信息分子网络对生物活性天然产物进行优先排序

天然产物代表了新型治疗剂的不竭来源。它们复杂而受约束的三维结构赋予这些分子以卓越的生物学特性,从而使它们在药物发现计划中发挥了重要作用。但是,寻找新的生物活性代谢物的过程因发现它们的生物基质的化学复杂性而受到阻碍。从此类基质中纯化单一成分需要进行大量工作,因此理想情况下应仅对具有高潜在价值的分子进行纯化(,化学新颖性和生物活性)。基于质谱代谢物谱分析方法的最新生物信息学方法开始解决复杂混合物中化合物鉴定的复杂任务。然而,与这些发展并行的是,仍然缺乏在天然产物分离之前提供有关天然产物生物活性潜力信息的方法,并且这些方法对于仅针对有价值的天然产物的分离具有重要的意义。在目前的调查中,我们提出了一种对生物活性天然产物进行优先排序的综合分析策略。我们的方法使用嵌入各种信息层(生物活性和分类数据)的大规模分子网络来突出显示原油提取物集合的化学多样性内潜在的生物活性支架。神经Bocquillonia nervosaNeoguillauminia cleopatra)针对两个生物学目标(Wnt信号通路和基孔肯雅病毒复制)。最终,详细描述了萘烷二萜原酸酯和四种抑制Wnt信号通路并表现出针对CHIKV病毒的有效抗病毒活性的12-脱氧佛波醇的检测和分离过程。结合有效的代谢物注释工具,该生物活性天然产物优先处理流程被证明是有效的。在基于天然提取物筛选的药物发现计划中实施此方法应加快生物活性天然产物的分离并使其合理化。
更新日期:2017-09-15
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