将实验与计算红外和质谱相结合,用于高通量非目标化学结构识别.
Erandika Karunaratne1, Dennis W Hill1, Kai Dührkop2
1Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, United States.
Analytical chemistry
|August 4, 2023
概括
将质谱学和红外光谱学与计算方法相结合,可以显著改善在非目标代谢学中识别未知的代谢物. 这种方法提高了环境和生物样本结构阐明的准确性和可靠性.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 代谢学 代谢学 代谢学
背景情况:
- 非目标代谢学与代谢物结构识别作斗争,限制了其应用.
- 目前的方法依赖于质谱和机器学习,搜索大量的化学数据库.
- 需要直角数据来提高识别率和可靠性.
研究的目的:
- 为了增强高通量非目标化学结构识别.
- 评估实验和计算质量和红外光谱数据的组合.
- 提高对候选结构进行验证的优先级.
主要方法:
- 获得了148种化合物的实验MS/MS和气相IR光谱.
- 为每个化合物从PubChem生成候选结构.
- 使用CSI:FingerID进行初始排名,随后进行DFT-IR预测和顶级候选人的排名.
- 根据这两种排名方法计算出一个复合得分.
主要成果:
- 在148种化合物中,88种化合物 (59%) 取得了正确的识别.
- 在前20名候选者中,在148种化合物中排名第129位 (87%).
- 报告了迄今为止使用PubChem候选结构的最高识别率.
结论:
- 结合实验和计算的MS/MS和IR光谱数据是有效的结构识别.
- 这种综合方法显著提高了候选结构的优先级.
- 该方法为推进非目标代谢学提供了一个强大的策略.
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