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从分析物的电子电离质谱推断其标称分子量。

Inferring the Nominal Molecular Mass of an Analyte from Its Electron Ionization Mass Spectrum.

作者信息

Moorthy Arun S, Kearsley Anthony J, Mallard William G, Wallace William E, Stein Stephen E

机构信息

Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.

Mathematical Analysis and Modeling Group, Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.

出版信息

Anal Chem. 2023 Sep 5;95(35):13132-13139. doi: 10.1021/acs.analchem.3c01815. Epub 2023 Aug 23.

DOI:10.1021/acs.analchem.3c01815
PMID:37610141
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10560098/
Abstract

The performance of three algorithms for predicting nominal molecular mass from an analyte's electron ionization mass spectrum is presented. The Peak Interpretation Method (PIM) attempts to quantify the likelihood that a molecular ion peak is contained in the mass spectrum, whereas the Simple Search Hitlist Method (SS-HM) and iterative Hybrid Search Hitlist Method (iHS-HM) leverage results from mass spectral library searching. These predictions can be employed in combination (recommended) or independently. The methods were tested on two sets of query mass spectra searched against libraries that did not contain the reference mass spectra of the same compounds: 19,074 spectra of various organic molecules searched against the NIST17 mass spectral library and 162 spectra of small molecule drugs searched against SWGDRUG version 3.3. Individually, each molecular mass prediction method had computed precisions (the fraction of positive predictions that were correct) of 91, 89, and 74%, respectively. The methods become more valuable when predictions are taken together. When all three predictions were identical, which occurred in 33% of the test cases, the predicted molecular mass was almost always correct (>99%).

摘要

本文介绍了三种从分析物的电子电离质谱预测标称分子量的算法的性能。峰解析法(PIM)试图量化质谱中包含分子离子峰的可能性,而简单搜索命中列表法(SS-HM)和迭代混合搜索命中列表法(iHS-HM)则利用质谱库搜索的结果。这些预测可以联合使用(推荐),也可以单独使用。这些方法在两组针对不包含相同化合物参考质谱的库进行搜索的查询质谱上进行了测试:19074个各种有机分子的质谱针对NIST17质谱库进行搜索,162个小分子药物的质谱针对SWGDRUG 3.3版进行搜索。单独来看,每种分子量预测方法的计算精度(正确的阳性预测比例)分别为91%、89%和74%。当综合考虑预测结果时,这些方法变得更有价值。当所有三个预测结果相同时(在33%的测试案例中出现),预测的分子量几乎总是正确的(>99%)。