化学
氘
质谱法
氢
碎片(计算)
同位素
质谱
分子
洗脱
分析化学(期刊)
色谱法
原子物理学
核物理学
有机化学
物理
操作系统
计算机科学
作者
Yury Kostyukevich,Alexander Zherebker,Alexey A. Orlov,Oxana A. Kovaleva,Tatyana I. Burykina,Boris N. Isotov,Е. Н. Николаев
标识
DOI:10.1021/acs.analchem.9b05379
摘要
Accurate and reliable identification of chemical compounds is the ultimate goal of mass spectrometry analyses. Currently, identification of compounds is usually based on the measurement of the accurate mass and fragmentation spectrum, chromatographic elution time, and collisional cross section. Unfortunately, despite the growth of databases of experimentally measured MS/MS spectra (such as MzCloud and Metlin) and developing software for predicting MS/MS fragments in silico from SMILES patterns (such as MetFrag, CFM-ID, and Ms-Finder), the problem of identification is still unsolved. The major issue is that the elution time and fragmentation spectra depend considerably on the equipment used and are not the same for different LC-MS systems. It means that any additional descriptors depending only on the structure of the chemical compound will be of big help for LC-MS/MS-based omics. Our approach is based on the characterization of compounds by the number of labile hydrogen and oxygen atoms in the molecule, which can be measured using hydrogen/deuterium and 16O/18O-exchange approaches. The number of labile atoms (those from -OH, -NH, ═O, and -COOH groups) can be predicted from SMILES patterns and serves as an additional structural descriptor when performing a database search. In addition, distribution of isotope labels among MS/MS fragments can be roughly predicted by software such as MetFrag or CFM-ID. Here, we present an approach utilizing the selection of structural candidates from a database on the basis of the number of functional groups and analysis of isotope labels distribution among fragments. It was found that our approach allows reduction of the search space by a factor of 10 and considerably increases the reliability of the compound identification.
科研通智能强力驱动
Strongly Powered by AbleSci AI