MSnLib:有效地生成开放的多阶段碎片化质谱库
Corinna Brungs1,2, Robin Schmid3,4, Steffen Heuckeroth5
1Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czechia.
Nature methods
|September 15, 2025
概括
一个新的开放资源,MSnLib,为30008个分子提供了超过200万个多阶段碎片化光谱. 这促进了化合物识别在代谢学和自然产品的发现.
科学领域:
- 分析化学 分析化学
- 生物化学 生物化学
- 计算化学计算化学
背景情况:
- 非定位的高分辨率质谱对于代谢学,自然产品发现和暴露学至关重要.
- 化合物识别是一个重大挑战,目前的方法依赖于光谱库匹配联质谱 (MS2) 数据.
研究的目的:
- 为了解决各种化学品缺乏开放的多阶段碎片化 (MSn) 参考数据的问题.
- 开发一个机器学习准备的资源,以改善化合物识别.
主要方法:
- 创建了MSnLib,这是一个包含超过200万个频谱的开源资源.
- 使用高通量数据采集和处理管道.
- 使用开源软件mzmine进行数据处理.
主要成果:
- MSnLib包含了MSn树,用于30008个独特的小分子.
- 该资源旨在为机器学习做好准备,促进先进的数据分析.
- 提供了对子结构的更深入的了解,并使碎片化途径的验证成为可能.
结论:
- MSnLib显著增强了用于MSn分析的可用参考数据.
- 预计这项资源将加速各种领域的化合物识别,包括代谢学和天然产品发现.
- 促进了碎片化路径和亚结构分析的验证.
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