化学
马兜铃酸
假阳性悖论
虚拟筛选
串联质谱法
碎片(计算)
串联
质谱
色谱法
质量
质谱法
生化工程
计算机科学
人工智能
计算化学
遗传学
生物
材料科学
工程类
复合材料
分子动力学
操作系统
作者
Xiaoqi Chen,Wenlin Wu,Hongbing Sun,Lu Chen,Yu Wang,Bing Xia,Yan Zhou
标识
DOI:10.1021/acs.analchem.3c04064
摘要
Aristolochic acid analogs (AAAs) are naturally occurring carcinogenic and toxic compounds that pose a safety threat to pharmaceuticals and the environment. It is challenging to screen AAAs due to their lack of characteristic mass spectral fragmentation and their presence of structural diversity. A comprehensive nontargeted screening strategy was proposed by taking into account diverse factors and incorporating various self-developed techniques, and a Python3-based toolkit called AAAs_finder was developed for its implementation. The main procedures consist of virtual structure and ultraviolet and visible (UV) spectra database creation, exact mass and UV spectra-based suspect data extraction, tandem mass spectra (MS/MS) anthropomorphic interpretation, and multicondition retention time (RT) prediction-based candidate structures ranking. To initially assess screening feasibility, eight hypothetical unknown samples were subjected to nontargeted screening using the AAAs_finder toolkit and two other advanced tools. The results showed that the former successfully identified all, while the latter two only managed to identify two and three, respectively, indicating that our strategy was more feasible. After that, the strategy was carefully evaluated for false positives and false negatives, instrument dependence, reproducibility, and sensitivity. After the evaluation, the strategy was successfully applied to the screening of AAAs in real samples, such as herbal medicine, spiked soil, and water. Overall, this study proposed a nontargeted screening strategy and toolkit independent of characteristic mass spectral fragmentation and able to overcome challenges posed by structural diversity for the AAAs screening, which is also valuable for other classes of compounds.
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