小柯机器人

美国加州大学戴维斯分校Oliver Fiehn课题组发现，光谱熵在用于小分子化合物鉴定时优于MS/MS点积相似度。相关论文于2021年12月2日在线发表在《自然—方法学》杂志上。

研究人员引入了串联质谱（MS/MS）光谱熵的概念，通过库匹配来改善MS/MS相似性搜索的评分结果。在针对高质量的NIST20库搜索434,287个光谱时，熵相似性优于42种替代相似性算法，包括点积相似性。事实证明，即使添加了不同水平的噪声离子，熵相似性得分也是高度稳健的。当研究人员对37,299个天然产物的实验光谱应用熵值时，在熵值相似度为0.75时，观察到的错误发现率低于10%。

实验性的人类肠道代谢组数据被用来证实熵相似性在很大程度上提高了小分子研究中基于质谱注释的准确性，使其错误发现率低于10%，注释了新的化合物，并为自动标记质量差、噪声大的光谱提供了依据。

据悉，小分子研究中的化合物鉴定，如非靶向代谢组学或暴露组研究，依赖于MS/MS光谱与实验或计算机质谱库的匹配。大多数软件程序使用点积相似度得分。

附：英文原文

Title: Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification

Author: Li, Yuanyue, Kind, Tobias, Folz, Jacob, Vaniya, Arpana, Mehta, Sajjan Singh, Fiehn, Oliver

Issue&Volume: 2021-12-02

Abstract: Compound identification in small-molecule research, such as untargeted metabolomics or exposome research, relies on matching tandem mass spectrometry (MS/MS) spectra against experimental or in silico mass spectral libraries. Most software programs use dot product similarity scores. Here we introduce the concept of MS/MS spectral entropy to improve scoring results in MS/MS similarity searches via library matching. Entropy similarity outperformed 42 alternative similarity algorithms, including dot product similarity, when searching 434,287 spectra against the high-quality NIST20 library. Entropy similarity scores proved to be highly robust even when we added different levels of noise ions. When we applied entropy levels to 37,299 experimental spectra of natural products, false discovery rates of less than 10% were observed at entropy similarity score 0.75. Experimental human gut metabolome data were used to confirm that entropy similarity largely improved the accuracy of MS-based annotations in small-molecule research to false discovery rates below 10%, annotated new compounds and provided the basis to automatically flag poor-quality, noisy spectra.

DOI: 10.1038/s41592-021-01331-z

Source: https://www.nature.com/articles/s41592-021-01331-z

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex