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利用预处理的质谱数据探索天然产物中的新型次生代谢产物。

Exploring novel secondary metabolites from natural products using pre-processed mass spectral data.

机构信息

College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Korea.

Laboratory of Natural Products Chemistry, College of Pharmacy, Kangwon National University, Chuncheon, 24341, Korea.

出版信息

Sci Rep. 2019 Nov 22;9(1):17430. doi: 10.1038/s41598-019-54078-1.

Abstract

Many natural product chemists are working to identify a wide variety of novel secondary metabolites from natural materials and are eager to avoid repeatedly discovering known compounds. Here, we developed liquid chromatography/mass spectrometry (LC/MS) data-processing protocols for assessing high-throughput spectral data from natural sources and scoring the novelty of unknown metabolites from natural products. This approach automatically produces representative MS spectra (RMSs) corresponding to single secondary metabolites in natural sources. In this study, we used the RMSs of Agrimonia pilosa roots and aerial parts as models to reveal the structural similarities of their secondary metabolites and identify novel compounds, as well as isolation of three types of nine new compounds including three pilosanidin- and four pilosanol-type molecules and two 3-hydroxy-3-methylglutaryl (HMG)-conjugated chromones. Furthermore, we devised a new scoring system, the Fresh Compound Index (FCI), which grades the novelty of single secondary metabolites from a natural material using an in-house database constructed from 466 representative medicinal plants from East Asian countries. We expect that the FCIs of RMSs in a sample will help natural product chemists to discover other compounds of interest with similar chemical scaffolds or novel compounds and will provide insights relevant to the structural diversity and novelty of secondary metabolites in natural products.

摘要

许多天然产物化学家致力于从天然材料中鉴定出各种各样的新型次生代谢产物,并渴望避免重复发现已知化合物。在这里,我们开发了液相色谱/质谱(LC/MS)数据处理方案,用于评估来自天然来源的高通量光谱数据,并对天然产物中未知代谢物的新颖性进行评分。该方法可自动生成与天然来源中单次生代谢物相对应的代表性 MS 光谱(RMS)。在这项研究中,我们使用龙牙草根和地上部分的 RMS 作为模型,揭示了它们次生代谢物的结构相似性,并鉴定出三种类型的 9 种新化合物,包括 3 种 Pilosanolidin 和 4 种 Pilosanol 型分子以及 2 种 3-羟基-3-甲基戊二酰基(HMG)-缀合色酮。此外,我们设计了一种新的评分系统,即新化合物指数(FCI),该系统使用来自东亚 466 种代表性药用植物的内部数据库,对天然产物中单次生代谢物的新颖性进行评分。我们期望样品中 RMS 的 FCI 将帮助天然产物化学家发现具有相似化学结构骨架的其他感兴趣的化合物或新型化合物,并为天然产物中次生代谢物的结构多样性和新颖性提供相关见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9558/6874550/21d10cd0bcf7/41598_2019_54078_Fig1_HTML.jpg

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