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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Main Authors: Xing, Shipei, Hu, Yan, Yin, Zixuan, Liu, Min, Tang, Xiaoyu, Fang, Mingliang, Huan, Tao
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/152068
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Institution: Nanyang Technological University
Language: English
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spelling 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.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Civil engineering
Biotransformation
Algorithms
spellingShingle 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
description 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.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Xing, Shipei
Hu, Yan
Yin, Zixuan
Liu, Min
Tang, Xiaoyu
Fang, Mingliang
Huan, Tao
format Article
author Xing, Shipei
Hu, Yan
Yin, Zixuan
Liu, Min
Tang, Xiaoyu
Fang, Mingliang
Huan, Tao
author_sort 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
publishDate 2021
url https://hdl.handle.net/10356/152068
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