评估测量精度对多维质谱,无参考代谢学中的代谢物识别概率的影响
Christine H Chang1, Sydney C Schwartz1, Alexandria K Im1
1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
Analytical chemistry
|June 25, 2025
概括
这项研究引入了一种新的计算方法,用于在质谱学中识别代谢物,扩大可识别化合物的数量. 它还为量化对这些代谢物识别的信心建立了一个框架.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 代谢学 代谢学 代谢学
背景情况:
- 基于质谱的代谢学中的化合物识别面临挑战,因为它依赖于真实的标准.
- 标准的有限可用性和分析吞吐量限制了可识别化合物的范围.
- 计算方法为"无引用"复合注释提供了一个新的范式.
研究的目的:
- 探索增强参考数据的新方法,用计算预测来扩展化合物识别.
- 系统地描述测量精度和识别概率之间的关系.
- 建立一个定量代谢物识别概率分析的框架.
主要方法:
- 利用计算方法 (理论驱动和基于AI/ML) 来预测质谱的分子性质.
- 量化描述了有机小分子代谢物测量精度和识别概率之间的关系.
- 开发了一种用于代谢物识别概率分析的框架.
主要成果:
- 计算预测显著扩大了可以识别的化学物种的宇宙,超出了当前的限制.
- 建立了测量精度和复合注释的可靠性之间的定量联系.
- 在定义的化学空间内证明了这种关系的系统性表征.
结论:
- 通过计算预测来增强参考数据,提高了质谱学中的代谢物识别能力.
- 鉴定信心评估的定量框架至关重要,因为搜索空间正在扩大.
- 这项工作为其他人量化代谢物识别概率提供了基础方法.
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