化学
提取器
天然产物
块(置换群论)
化学空间
组合化学
计算生物学
药物发现
数据挖掘
立体化学
计算机科学
生物化学
工艺工程
几何学
数学
工程类
生物
作者
Dafu Zhu,Jie Yuan,Qun‐Ying Yue,Yinggong Zhao,Shengping Yu,Abilova Zhamilya,Janar Jenis,Chunping Tang,Bo Wang,Jia Liu,Yuxiang Yang
标识
DOI:10.1021/acs.analchem.3c00744
摘要
The utilization of a building-block-based molecular network is an efficient approach to investigate the unknown chemical space of natural products. However, structure-based automated MS/MS data mining remains challenging. This study introduces building block extractor, a user-friendly MS/MS data mining program that automatically extracts user-defined specified features. In addition to the characteristic product ions and neutral losses, this program integrates the abundance of the product ions and sequential neutral loss features as building blocks for the first time. The discovery of nine undescribed sesquiterpenoid dimers from Artemisia heptapotamica highlights the power of this tool. One of these dimers, artemiheptolide I (9), exhibited in vitro inhibition of influenza A/Hongkong/8/68 (H3N2) with an IC50 of 8.01 ± 6.19 μM. Furthermore, two known guaianolide derivatives (16 and 17) possessed remarkable antiviral activity against influenza A/Puerto Rico/8/1934 H1N1, H3N2, and influenza B/Lee/40 with IC50 values ranging from 3.46 to 11.77 μM. In addition to the efficient discovery of novel natural products, this strategy can be generally applied to grab derivatives with specific fragments and enhance the annotation power of LC-MS/MS analysis.
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