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Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics
Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891523/ https://www.ncbi.nlm.nih.gov/pubmed/36668672 http://dx.doi.org/10.1371/journal.pcbi.1010457 |
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author | Gueto-Tettay, Carlos Tang, Di Happonen, Lotta Heusel, Moritz Khakzad, Hamed Malmström, Johan Malmström, Lars |
author_facet | Gueto-Tettay, Carlos Tang, Di Happonen, Lotta Heusel, Moritz Khakzad, Hamed Malmström, Johan Malmström, Lars |
author_sort | Gueto-Tettay, Carlos |
collection | PubMed |
description | Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models’ performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set’s size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2–3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs’ proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field. |
format | Online Article Text |
id | pubmed-9891523 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98915232023-02-02 Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics Gueto-Tettay, Carlos Tang, Di Happonen, Lotta Heusel, Moritz Khakzad, Hamed Malmström, Johan Malmström, Lars PLoS Comput Biol Research Article Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models’ performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set’s size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2–3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs’ proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field. Public Library of Science 2023-01-20 /pmc/articles/PMC9891523/ /pubmed/36668672 http://dx.doi.org/10.1371/journal.pcbi.1010457 Text en © 2023 Gueto-Tettay et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Gueto-Tettay, Carlos Tang, Di Happonen, Lotta Heusel, Moritz Khakzad, Hamed Malmström, Johan Malmström, Lars Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics |
title | Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics |
title_full | Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics |
title_fullStr | Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics |
title_full_unstemmed | Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics |
title_short | Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics |
title_sort | multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891523/ https://www.ncbi.nlm.nih.gov/pubmed/36668672 http://dx.doi.org/10.1371/journal.pcbi.1010457 |
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