加拿大英属哥伦比亚大学Tao Huan团队近期取得重要工作进展，他们研究开发了BUDDY，可以通过自下而上的MS/MS查询发现分子式。相关研究成果2023年4月13日在线发表于《自然—方法学》杂志上。

据介绍，在基于质谱（MS）的代谢组学中，相当一部分代谢特征尚未确定，分子式注释是揭示其化学特性的起点。

研究人员提出了自下而上的串联MS（MS/MS）查询方式，这是一种从头开始的公式注释方法。这一方法优先考虑MS/MS可解释的候选公式，实现机器学习排名，并提供错误发现率估计。与数学上穷举的公式枚举相比，这一方法平均将公式候选空间缩小了42.8%。方法在参考MS/MS文库和真实代谢组学数据集上系统地进行注释准确性的基准测试。

应用于155321个重复的未识别光谱，这一新的方法准确注释了化学数据库中没有的5000个新分子式。除了个体代谢特征的水平外，研究人员将自下而上的MS/MS询问与全局优化相结合，以完善公式注释，同时揭示峰值相互关系。这种方法允许对人类粪便数据中的37个脂肪酸酰胺分子进行系统注释。所有生物信息学管道都可以在一个独立的软件BUDDY中使用（https://github.com/HuanLab/BUDDY）。

附：英文原文

Title: BUDDY: molecular formula discovery via bottom-up MS/MS interrogation

Author: Xing, Shipei, Shen, Sam, Xu, Banghua, Li, Xiaoxiao, Huan, Tao

Issue&Volume: 2023-04-13

Abstract: A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY (https://github.com/HuanLab/BUDDY).

DOI: 10.1038/s41592-023-01850-x

Source: https://www.nature.com/articles/s41592-023-01850-x