一个分子离子的红外光谱图书馆用于代谢物识别
Kas J Houthuijs1, Giel Berden1, Udo F H Engelke2
1Institute for Molecules and Materials, FELIX Laboratory, Radboud University, Nijmegen 6525 ED, The Netherlands.
Analytical chemistry
|June 1, 2023
概括
一个新的红外离子光谱学 (IRIS) 光谱计算库有助于识别小分子. 这种工具与质谱学 (MS) 结合,在实验数据中成功识别了75%的代谢物,使得未知化合物的发现成为可能.
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:
- 红外离子光谱学 (IRIS) 与质谱学 (MS) 结合,是用于小分子识别的强大工具.
- 一个主要的限制是缺乏针对分子离子的IRIS光谱的全面参考库.
- 量子化学计算的光谱提供了一个可行的替代方案,但候选结构的选择仍然是一个挑战.
研究的目的:
- 开发一个in silico图书馆的振动光谱常见的MS添加人体代谢组合物的化合物.
- 使用IRIS实验数据验证自动化光谱匹配程序.
- 展示一种用于基结构识别和未知化合物的 de novo识别的方法.
主要方法:
- 创建一个 in silico 库,包含超过 75,000 个计算的振动光谱 (DFT 级),用于 >4500 种人类代谢组合物.
- 使用IRIS实验光谱数据库 (189个光谱) 开发和验证自动化光谱匹配算法.
- 图书馆的应用程序用于m/z受约束的识别和m/z不受约束的子结构搜索.
主要成果:
- 在 silico 库中包含了 >75,000 个计算的 IRIS 光谱,用于 >4500 个化合物.
- 基于m/z和IRIS频谱的实验数据集中,自动匹配正确识别了75%的代谢物.
- 成功地确定了未知的血代谢物 (3-基酸) 的新发现.
结论:
- 开发的in silico光谱库显著提高了IRIS-MS用于小分子识别的能力.
- 该方法能够准确识别已知的代谢物,并促进发现未知的化合物.
- 这种方法代表了代谢学和结构阐释的重大进步.
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