Xing Shipei, Hu Yan, Yin Zixuan, Liu Min, Tang Xiaoyu, Fang Mingliang, Huan Tao
Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 BC, Canada.
Department of Computer Sciences, University of British Columbia, 2366 Main Mall, Vancouver, V6T 1Z1 BC, Canada.
Anal Chem. 2020 Nov 3;92(21):14476-14483. doi: 10.1021/acs.analchem.0c02521. Epub 2020 Oct 19.
Spectral similarity comparison through tandem mass spectrometry (MS) is a powerful approach to annotate known and unknown metabolic features in mass spectrometry (MS)-based untargeted metabolomics. In this work, we proposed the concept of hypothetical neutral loss (HNL), which is the mass difference between a pair of fragment ions in a MS spectrum. We demonstrated that HNL values contain core structural information that can be used to accurately assess the structural similarity between two MS spectra. We then developed the Core Structure-based Search (CSS) algorithm based on HNL values. CSS was validated with sets of hundreds of randomly selected metabolites and their reference MS spectra, showing significantly improved correlation between spectral and structural similarities. Compared to state-of-the-art spectral similarity algorithms, CSS generates better ranking of structurally relevant chemicals among false positives. Combining CSS, HNL library, and biotransformation database, we further developed Metabolite core structure-based Search (McSearch), a novel computational solution to facilitate the annotation of unknown metabolites using the reference MS spectra of their structural analogs. McSearch generates better results in the Critical Assessment of Small Molecule Identification (CASMI) 2017 data set than conventional unknown feature annotation programs. McSearch was also tested in experimental MS data of xenobiotic metabolite derivatives belonging to three different metabolic pathways. Our results confirmed that McSearch can better capture the underlying structural similarity between MS spectra. Overall, this work provides a novel direction for metabolite annotation via HNL values, paving the way for annotating metabolites using their structurally similar compounds.
通过串联质谱（MS）进行光谱相似性比较是一种强大的方法，可用于在基于质谱（MS）的非靶向代谢组学中注释已知和未知的代谢特征。在这项工作中，我们提出了假设中性损失（HNL）的概念，它是质谱图中一对碎片离子之间的质量差。我们证明HNL值包含核心结构信息，可用于准确评估两个质谱图之间的结构相似性。然后，我们基于HNL值开发了基于核心结构的搜索（CSS）算法。CSS通过数百个随机选择的代谢物及其参考质谱图进行了验证，结果表明光谱相似性和结构相似性之间的相关性显著提高。与最先进的光谱相似性算法相比，CSS在误报中对结构相关化学物质的排名更好。结合CSS、HNL库和生物转化数据库，我们进一步开发了基于代谢物核心结构的搜索（McSearch），这是一种新颖的计算解决方案，可利用其结构类似物的参考质谱图来促进未知代谢物的注释。在2017年小分子识别关键评估（CASMI）数据集中，McSearch比传统的未知特征注释程序产生了更好的结果。McSearch还在属于三种不同代谢途径的外源代谢物衍生物的实验质谱数据中进行了测试。我们的结果证实，McSearch可以更好地捕捉质谱图之间潜在的结构相似性。总体而言，这项工作为通过HNL值进行代谢物注释提供了一个新方向，为利用结构相似化合物注释代谢物铺平了道路。
