一种分子网络辅助的自动数据库选策略,用于在复杂矩阵中对小分子进行全面的注释
Xin-Lu Li1, Zi-Fan Guo1, Xiao-Dong Wen1
1State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing 210009, China.
Journal of chromatography. A
|October 1, 2023
概括
我们开发了一个新的策略,分子网络辅助自动数据库选 (MN/自动DBS),以识别复杂样品中的小分子. 这种方法成功地在黄-丹申草药对中发现了223种化合物,其中包括65种新型化合物.
科学领域:
- 分析化学 分析化学
- 代谢学 代谢学 代谢学
- 自然产品化学 自然产品化学
背景情况:
- 使用LCHRMS在复杂矩阵中注释小分子是具有挑战性的,因为数据的复杂性和有限的参考光谱.
- 现有的方法难以全面识别已知和未知的化合物.
研究的目的:
- 开发和验证一种新的策略,即MN/自动DBS,用于在复杂样品中高效准确的小分子注释.
- 改进报告和未报告化合物的识别.
主要方法:
- 开发了一个Python软件来创建一个内部数据库 (DB) 用于自动选.
- 通过基于特征的分子网络 (FBMN) 使用的MS1精确质量选与MS2相似性分析相结合.
- 自动选和分子网络的综合结果,其次是手动选.
主要成果:
- 在黄-丹申 (HD) 草药对中成功注释了223种化合物.
- 鉴定了65种以前未在HD中报告的化合物,包括芳香多基基化物,氨酸和二甲胺.
- 证明了MN/自动DBS策略在分析复杂矩阵和发现新化合物的有效性.
结论:
- MN/自动DBS策略显著提高了复杂的天然产品样本中小分子的注释.
- 这种方法促进了新型化合物的发现,并提供了全面的化学概况.
- MN/自动DBS适用于挖掘潜在的新化学实体的各种复杂矩阵.
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