Department of Chemistry, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada.
J Nat Prod. 2021 Apr 23;84(4):1044-1055. doi: 10.1021/acs.jnatprod.0c01076. Epub 2021 Mar 22.
The development of new "omics" platforms is having a significant impact on the landscape of natural products discovery. However, despite the advantages that such platforms bring to the field, there remains no straightforward method for characterizing the chemical landscape of natural products libraries using two-dimensional nuclear magnetic resonance (2D-NMR) experiments. NMR analysis provides a powerful complement to mass spectrometric approaches, given the universal coverage of NMR experiments. However, the high degree of signal overlap, particularly in one-dimensional NMR spectra, has limited applications of this approach. To address this issue, we have developed a new data analysis platform for complex mixture analysis, termed MADByTE (Metabolomics and Dereplication by Two-Dimensional Experiments). This platform employs a combination of TOCSY and HSQC spectra to identify spin system features within complex mixtures and then matches spin system features between samples to create a chemical similarity network for a given sample set. In this report we describe the design and construction of the MADByTE platform and demonstrate the application of chemical similarity networks for both the dereplication of known compound scaffolds and the prioritization of bioactive metabolites from a bacterial prefractionated extract library.
新的“组学”平台的发展正在对天然产物发现领域产生重大影响。然而，尽管这些平台为该领域带来了诸多优势，但仍然没有一种简单的方法可以使用二维核磁共振（2D-NMR）实验来描述天然产物文库的化学特征。鉴于 NMR 实验的普遍适用性，NMR 分析为质谱方法提供了有力的补充。然而，由于一维 NMR 光谱中信号重叠程度很高，这种方法的应用受到限制。为了解决这个问题，我们开发了一种新的用于复杂混合物分析的数据分析平台，称为 MADByTE（通过二维实验进行代谢组学和去重复）。该平台采用 TOCSY 和 HSQC 光谱的组合来识别复杂混合物中的自旋系统特征，然后在样品之间匹配自旋系统特征，为给定的样品集创建化学相似性网络。在本报告中，我们描述了 MADByTE 平台的设计和构建，并展示了化学相似性网络在已知化合物支架去重复和细菌预分级提取物文库中生物活性代谢物优先级排序方面的应用。
