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Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides
The Feature-based Molecular Networking (FBMN) is a well-known approach for mapping and identifying structures and analogues. However, in the absence of prior knowledge about the molecular class, assessing specific fragments and clusters requires time-consuming manual validation. This study demonstra...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427501/ https://www.ncbi.nlm.nih.gov/pubmed/37593127 http://dx.doi.org/10.3389/fmolb.2023.1238475 |
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author | Selegato, Denise M. Zanatta, Ana C. Pilon, Alan C. Veloso, Juvenal H. Castro-Gamboa, Ian |
author_facet | Selegato, Denise M. Zanatta, Ana C. Pilon, Alan C. Veloso, Juvenal H. Castro-Gamboa, Ian |
author_sort | Selegato, Denise M. |
collection | PubMed |
description | The Feature-based Molecular Networking (FBMN) is a well-known approach for mapping and identifying structures and analogues. However, in the absence of prior knowledge about the molecular class, assessing specific fragments and clusters requires time-consuming manual validation. This study demonstrates that combining FBMN and Mass Spec Query Language (MassQL) is an effective strategy for accelerating the decoding mass fragmentation pathways and identifying molecules with comparable fragmentation patterns, such as beauvericin and its analogues. To accomplish this objective, a spectral similarity network was built from ESI-MS/MS experiments of Fusarium oxysporum at various collision energies (CIDs) and paired with a MassQL search query for conserved beauvericin ions. FBMN analysis revealed that sodiated and protonated ions clustered differently, with sodiated adducts needing more collision energy and exhibiting a distinct fragmentation pattern. Based on this distinction, two sets of particular fragments were discovered for the identification of these hexadepsipeptides: ([M + H](+)) m/z 134, 244, 262, and 362 and ([M + Na](+)) m/z 266, 284 and 384. By using these fragments, MassQL accurately found other analogues of the same molecular class and annotated beauvericins that were not classified by FBMN alone. Furthermore, FBMN analysis of sodiated beauvericins at 70 eV revealed subclasses with distinct amino acid residues, allowing distinction between beauvericins (beauvericin and beauvericin D) and two previously unknown structural isomers with an unusual methionine sulfoxide residue. In summary, our integrated method revealed correlations between adduct types and fragmentation patterns, facilitated the detection of beauvericin clusters, including known and novel analogues, and allowed for the differentiation between structural isomers. |
format | Online Article Text |
id | pubmed-10427501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104275012023-08-17 Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides Selegato, Denise M. Zanatta, Ana C. Pilon, Alan C. Veloso, Juvenal H. Castro-Gamboa, Ian Front Mol Biosci Molecular Biosciences The Feature-based Molecular Networking (FBMN) is a well-known approach for mapping and identifying structures and analogues. However, in the absence of prior knowledge about the molecular class, assessing specific fragments and clusters requires time-consuming manual validation. This study demonstrates that combining FBMN and Mass Spec Query Language (MassQL) is an effective strategy for accelerating the decoding mass fragmentation pathways and identifying molecules with comparable fragmentation patterns, such as beauvericin and its analogues. To accomplish this objective, a spectral similarity network was built from ESI-MS/MS experiments of Fusarium oxysporum at various collision energies (CIDs) and paired with a MassQL search query for conserved beauvericin ions. FBMN analysis revealed that sodiated and protonated ions clustered differently, with sodiated adducts needing more collision energy and exhibiting a distinct fragmentation pattern. Based on this distinction, two sets of particular fragments were discovered for the identification of these hexadepsipeptides: ([M + H](+)) m/z 134, 244, 262, and 362 and ([M + Na](+)) m/z 266, 284 and 384. By using these fragments, MassQL accurately found other analogues of the same molecular class and annotated beauvericins that were not classified by FBMN alone. Furthermore, FBMN analysis of sodiated beauvericins at 70 eV revealed subclasses with distinct amino acid residues, allowing distinction between beauvericins (beauvericin and beauvericin D) and two previously unknown structural isomers with an unusual methionine sulfoxide residue. In summary, our integrated method revealed correlations between adduct types and fragmentation patterns, facilitated the detection of beauvericin clusters, including known and novel analogues, and allowed for the differentiation between structural isomers. Frontiers Media S.A. 2023-08-01 /pmc/articles/PMC10427501/ /pubmed/37593127 http://dx.doi.org/10.3389/fmolb.2023.1238475 Text en Copyright © 2023 Selegato, Zanatta, Pilon, Veloso and Castro-Gamboa. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Selegato, Denise M. Zanatta, Ana C. Pilon, Alan C. Veloso, Juvenal H. Castro-Gamboa, Ian Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides |
title | Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides |
title_full | Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides |
title_fullStr | Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides |
title_full_unstemmed | Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides |
title_short | Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides |
title_sort | application of feature-based molecular networking and massql for the ms/ms fragmentation study of depsipeptides |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427501/ https://www.ncbi.nlm.nih.gov/pubmed/37593127 http://dx.doi.org/10.3389/fmolb.2023.1238475 |
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