Investigation of the chemical compounds in Pheretima aspergillum (E. Perrier) using a combination of mass spectral molecular networking and unsupervised substructure annotation topic modeling together with in silico fragmentation prediction

工作流程 化学 下部结构 生物信息学 碎片(计算) 质谱法 四极飞行时间 注释 化学空间 计算生物学 生物系统 计算机科学 人工智能 串联质谱法 色谱法 药物发现 数据库 操作系统 工程类 基因 生物 结构工程 生物化学
作者
Tao-Fang Cheng,Yuhao Zhang,Ji Ye,Hui‐Zi Jin,Weidong Zhang
出处
期刊:Journal of Pharmaceutical and Biomedical Analysis [Elsevier]
卷期号:184: 113197-113197 被引量:20
标识
DOI:10.1016/j.jpba.2020.113197
摘要

Untargeted mass spectrometry analysis is one of the most challenging and meaningful steps in the rapid structural elucidation of the highly complex and diverse constituents of traditional Chinese medicine. Specifically, it is a laborious and time-consuming way to identify unknown compounds. Herein, a workflow was proposed to expedite the annotations of the chemical structures in Pheretima aspergillum (E. Perrier) (Di-Long, DL). First, ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOFMS) was performed to obtain the untargeted mass spectral data. Then, the spectral data were uploaded to the Global Natural Products Social Molecular Networking (GNPS) platform to create a network and extract the Mass2Motifs (co-occurring fragments and neutral losses) using unsupervised substructure annotation topic modeling (MS2LDA). Finally, a structural analysis was performed using the proposed workflow of MS2LDA in combination with mass spectral molecular networking and in silico fragmentation prediction. As a result, a total of 124 compounds from DL were effectively characterized, of which 89 (7 furan sulfonic acids, 57 phospholipids and 25 carboxamides) were identified as potentially new compounds from DL. The results presented in this article significantly improve the understanding of the chemical composition of DL and provide a solid scientific basis for the future study of the quality control, underlying pharmacology and mechanism of DL. Moreover, the proposed workflow was used for the first time to accelerate the annotations of unknown molecules from TCM. Furthermore, this workflow will increase the efficiency of characterizing the ‘unknown knowns’ and elucidation of the ‘unknown unknowns’ from TCM, which are crucial steps of discovering the natural product drugs in TCM.
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