Development of an NMR-Based Platform for the Direct Structural Annotation of Complex Natural Products Mixtures.
Development of an NMR-Based Platform for the Direct Structural Annotation of Complex Natural Products Mixtures.
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DOI：
10.1021/acs.jnatprod.0c01076
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发表时间：
2021-04-23
影响因子：
5.1
通讯作者：
Linington RG
中科院分区：
文献类型：
--
作者：
Egan JM;van Santen JA;Liu DY;Linington RG
新“组学”平台的开发正在对天然产物发现领域产生重大影响。然而，尽管此类平台给该领域带来了诸多优势，但仍然没有直接的方法来使用二维核磁共振（2D-NMR）实验来表征天然产物库的化学景观。鉴于 NMR 实验的普遍覆盖，NMR 分析为质谱方法提供了强有力的补充。然而，高度的信号重叠，特别是在一维核磁共振谱中，限制了这种方法的应用。为了解决这个问题，我们开发了一种用于复杂混合物分析的新数据分析平台，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，然后匹配样品之间的自旋系统特征，为给定样品组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，并演示了化学相似性网络在已知化合物支架的去重复和细菌预分级提取物库中生物活性代谢物的优先排序中的应用。
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.
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影响因子：
5.1
作者：
Britton ER;Kellogg JJ;Kvalheim OM;Cech NB
通讯作者：
Cech NB
影响因子：
4
作者：
Bingol, Kerem;Li, Da-Wei;Bruschweiler-Li, Lei;Cabrera, Oscar A.;Megraw, Timothy;Zhang, Fengli;Brueschweiler, Rafael
通讯作者：
Brueschweiler, Rafael
影响因子：
2.7
作者：
Helmus JJ;Jaroniec CP
通讯作者：
Jaroniec CP
影响因子：
6.2
作者：
Gu H;Pan Z;Xi B;Asiago V;Musselman B;Raftery D
通讯作者：
Raftery D
影响因子：
4.1
作者：
Kuhn, Stefan;Johnson, Sean R.
通讯作者：
Johnson, Sean R.