Covington Brett C, McLean John A, Bachmann Brian O
Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Nashville, TN 37235, USA.
Nat Prod Rep. 2017 Jan 4;34(1):6-24. doi: 10.1039/c6np00048g.
Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.
2000年至2016年
微生物天然产物发现的劳动密集型过程取决于在复杂的生物提取物中识别出感兴趣的离散次级代谢产物,这些提取物包含生物体或菌群产生的所有可提取小分子的清单。从历史上看,化合物分离的优先级是由观察到的生物活性和/或相对代谢产物丰度驱动的,随后通过精确质量分析进行重复排除。使用这些方法的变体进行了数十年的发现,产生了天然药典,但也导致了近期较高的重新发现率。然而,基因组测序揭示了先前挖掘的生物体中大量未开发的潜力,并可以为潜在的新次级代谢产物提供有用的先见之明,最终实现分离。最近,比较代谢组学分析的进展已与次级代谢预测相结合,以加速与生物活性和丰度无关的发现工作流程。在本综述中,我们将讨论使基于质谱的代谢组学应用于天然产物发现的各种分析和计算技术,并讨论比较代谢组学在天然产物发现中的未来前景。