超快速和精确的电子电离质谱匹配用于化合物识别,具有百万级的体图书馆
Qiong Yang1, Hongchao Ji2, Zhenbo Xu1
1College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, PR, China.
Nature communications
|June 22, 2023
概括
本研究介绍了FastEI,这是一种超快速和精确的频谱匹配方法,用于质谱 (MS) 化合物识别. 它利用一个大型的in-silico库和先进的算法来显著提高速度和准确性,帮助研究人员识别未知的化合物.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 生物化学 生物化学
背景情况:
- 频谱匹配对于质谱学 (MS) 中的化合物识别至关重要.
- 现有的方法在光谱库覆盖范围,准确性和匹配速度方面面临挑战.
- 需要更有效,更准确的MS识别工具.
研究的目的:
- 开发用于质谱的超快速和精确的频谱匹配方法.
- 建立一个百万级的in-silico EI-MS光谱库.
- 创建用于化合物识别的用户友好的软件.
主要方法:
- 建立了一个百万级的in-silico电子电离质谱 (EI-MS) 图书馆.
- 开发了使用Word2vec光谱嵌入的FastEI方法,以提高准确性.
- 实现了分层可导航的小世界图 (HNSW) 以加速匹配.
- 将FastEI性能与加权的等号相似性 (WCS) 进行比较.
主要成果:
- FastEI实现了80.4%的回忆@10精度 (88.3%与5Da质量过器).
- 与WCS相比,演示了两个数量级的加速.
- 在NIST 2017图书馆之外的分子中实现了50%的回忆@1准确性.
- 包装FastEI作为一个用户友好的独立软件.
结论:
- FastEI显著提高了质谱学中化合物识别的准确性和速度.
- 结合FastEI和大型in-silico库,为研究人员提供了一个强大的工具.
- 该软件为具有有限计算专业知识的用户提供复合物识别的便利.
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