Retrieving and utilizing hypothetical neutral losses from tandem mass spectra for spectral similarity analysis and unknown metabolite annotation
Spectral similarity comparison through tandem mass spectrometry (MS²) 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 differenc...
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sg-ntu-dr.10356-1520682021-09-01T06:43:15Z Retrieving and utilizing hypothetical neutral losses from tandem mass spectra for spectral similarity analysis and unknown metabolite annotation Xing, Shipei Hu, Yan Yin, Zixuan Liu, Min Tang, Xiaoyu Fang, Mingliang Huan, Tao School of Civil and Environmental Engineering Engineering::Civil engineering Biotransformation Algorithms Spectral similarity comparison through tandem mass spectrometry (MS²) 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 MS² spectrum. We demonstrated that HNL values contain core structural information that can be used to accurately assess the structural similarity between two MS² 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 MS² 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 MS² 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 MS² data of xenobiotic metabolite derivatives belonging to three different metabolic pathways. Our results confirmed that McSearch can better capture the underlying structural similarity between MS² 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. This study was funded by the University of British Columbia Start-Up Grant, Canada Foundation for Innovation, BC Knowledge Development Fund, UBC Support for Teams to Advance Interdisciplinary Research Award, and National Science and Engineering Research Council Discovery Grant. 2021-09-01T06:43:15Z 2021-09-01T06:43:15Z 2020 Journal Article Xing, S., Hu, Y., Yin, Z., Liu, M., Tang, X., Fang, M. & Huan, T. (2020). Retrieving and utilizing hypothetical neutral losses from tandem mass spectra for spectral similarity analysis and unknown metabolite annotation. Analytical Chemistry, 92(21), 14476-14483. https://dx.doi.org/10.1021/acs.analchem.0c02521 0003-2700 https://hdl.handle.net/10356/152068 10.1021/acs.analchem.0c02521 33076659 2-s2.0-85096093910 21 92 14476 14483 en Analytical Chemistry © 2020 American Chemical Society. All rights reserved. |
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Engineering::Civil engineering Biotransformation Algorithms Xing, Shipei Hu, Yan Yin, Zixuan Liu, Min Tang, Xiaoyu Fang, Mingliang Huan, Tao Retrieving and utilizing hypothetical neutral losses from tandem mass spectra for spectral similarity analysis and unknown metabolite annotation |
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Spectral similarity comparison through tandem mass spectrometry (MS²) 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 MS² spectrum. We demonstrated that HNL values contain core structural information that can be used to accurately assess the structural similarity between two MS² 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 MS² 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 MS² 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 MS² data of xenobiotic metabolite derivatives belonging to three different metabolic pathways. Our results confirmed that McSearch can better capture the underlying structural similarity between MS² 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. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Xing, Shipei Hu, Yan Yin, Zixuan Liu, Min Tang, Xiaoyu Fang, Mingliang Huan, Tao |
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Article |
author |
Xing, Shipei Hu, Yan Yin, Zixuan Liu, Min Tang, Xiaoyu Fang, Mingliang Huan, Tao |
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Xing, Shipei |
title |
Retrieving and utilizing hypothetical neutral losses from tandem mass spectra for spectral similarity analysis and unknown metabolite annotation |
title_short |
Retrieving and utilizing hypothetical neutral losses from tandem mass spectra for spectral similarity analysis and unknown metabolite annotation |
title_full |
Retrieving and utilizing hypothetical neutral losses from tandem mass spectra for spectral similarity analysis and unknown metabolite annotation |
title_fullStr |
Retrieving and utilizing hypothetical neutral losses from tandem mass spectra for spectral similarity analysis and unknown metabolite annotation |
title_full_unstemmed |
Retrieving and utilizing hypothetical neutral losses from tandem mass spectra for spectral similarity analysis and unknown metabolite annotation |
title_sort |
retrieving and utilizing hypothetical neutral losses from tandem mass spectra for spectral similarity analysis and unknown metabolite annotation |
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2021 |
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https://hdl.handle.net/10356/152068 |
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1710686917434015744 |