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MSNovelist: de novo structure generation from mass spectra
Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder–decoder neural network to generate...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262714/ https://www.ncbi.nlm.nih.gov/pubmed/35637304 http://dx.doi.org/10.1038/s41592-022-01486-3 |
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author | Stravs, Michael A. Dührkop, Kai Böcker, Sebastian Zamboni, Nicola |
author_facet | Stravs, Michael A. Dührkop, Kai Böcker, Sebastian Zamboni, Nicola |
author_sort | Stravs, Michael A. |
collection | PubMed |
description | Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder–decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS(2)) spectra. In an evaluation with 3,863 MS(2) spectra from the Global Natural Product Social Molecular Networking site, MSNovelist predicted 25% of structures correctly on first rank, retrieved 45% of structures overall and reproduced 61% of correct database annotations, without having ever seen the structure in the training phase. Similarly, for the CASMI 2016 challenge, MSNovelist correctly predicted 26% and retrieved 57% of structures, recovering 64% of correct database annotations. Finally, we illustrate the application of MSNovelist in a bryophyte MS(2) dataset, in which de novo structure prediction substantially outscored the best database candidate for seven spectra. MSNovelist is ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds. |
format | Online Article Text |
id | pubmed-9262714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-92627142022-07-09 MSNovelist: de novo structure generation from mass spectra Stravs, Michael A. Dührkop, Kai Böcker, Sebastian Zamboni, Nicola Nat Methods Article Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder–decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS(2)) spectra. In an evaluation with 3,863 MS(2) spectra from the Global Natural Product Social Molecular Networking site, MSNovelist predicted 25% of structures correctly on first rank, retrieved 45% of structures overall and reproduced 61% of correct database annotations, without having ever seen the structure in the training phase. Similarly, for the CASMI 2016 challenge, MSNovelist correctly predicted 26% and retrieved 57% of structures, recovering 64% of correct database annotations. Finally, we illustrate the application of MSNovelist in a bryophyte MS(2) dataset, in which de novo structure prediction substantially outscored the best database candidate for seven spectra. MSNovelist is ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds. Nature Publishing Group US 2022-05-30 2022 /pmc/articles/PMC9262714/ /pubmed/35637304 http://dx.doi.org/10.1038/s41592-022-01486-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Stravs, Michael A. Dührkop, Kai Böcker, Sebastian Zamboni, Nicola MSNovelist: de novo structure generation from mass spectra |
title | MSNovelist: de novo structure generation from mass spectra |
title_full | MSNovelist: de novo structure generation from mass spectra |
title_fullStr | MSNovelist: de novo structure generation from mass spectra |
title_full_unstemmed | MSNovelist: de novo structure generation from mass spectra |
title_short | MSNovelist: de novo structure generation from mass spectra |
title_sort | msnovelist: de novo structure generation from mass spectra |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262714/ https://www.ncbi.nlm.nih.gov/pubmed/35637304 http://dx.doi.org/10.1038/s41592-022-01486-3 |
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