高分辨率MS光谱图书馆的计算扩展
Brandon Y Lieng1,2,3, Andrew T Quaile1,2,3, Xavier Domingo-Almenara4,5
1Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada.
Analytical chemistry
|November 14, 2023
概括
本研究介绍了一种计算方法,用于生成高分辨率的MSn光谱,以改善在非目标代谢学中代谢物识别. 该方法增强了光谱库,有助于解决复杂的分子结构.
科学领域:
- 分析化学 分析化学
- 代谢学 代谢学 代谢学
- 计算化学计算化学
背景情况:
- 在非向的代谢学中,代谢物识别通常受到异酸和结构相似的化合物的阻碍.
- 传统的MS2分析提供有限的结构信息,需要先进的技术,如MSn碎片化.
- 高分辨率的MSn光谱对于精确的质量确定至关重要,但在现有的光谱库中很少.
研究的目的:
- 从低分辨率数据生成高分辨率MSn光谱的计算方法.
- 通过扩展光谱库内容来增强代谢物识别工作流程.
- 为了解决由于光谱模两可的现有代谢学数据分析的局限性.
主要方法:
- 开发了一种计算方法,将低分辨率的MSn光谱转换为高分辨率的光谱.
- 通过束式碰撞激活解离 (CAD) 生成的补充高分辨率MS2光谱被使用.
- 作为光谱数据转换的来源,使用了NIST20 MS/MS数据库.
主要成果:
- 成功生成了高分辨率MSn光谱树的数据库.
- 数据库包括9637种化合物和19386种前体离子.
- 超过90%的信号强度从低分辨率转换为高分辨率的MSn光谱,证明了该方法的有效性.
结论:
- 开发的计算方法有效生成高分辨率的MSn光谱,显著提高光谱库的完整性.
- 这一进步有助于在非目标代谢学中更有信心地识别代谢物.
- 这种方法为研究人员处理复杂的代谢样本和光谱数据挑战提供了宝贵的工具.
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