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DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning
[Image: see text] Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the peptide itself, such as its amino...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401496/ https://www.ncbi.nlm.nih.gov/pubmed/37459568 http://dx.doi.org/10.1021/acs.analchem.3c00460 |
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author | Son, Juho Na, Seungjin Paek, Eunok |
author_facet | Son, Juho Na, Seungjin Paek, Eunok |
author_sort | Son, Juho |
collection | PubMed |
description | [Image: see text] Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the peptide itself, such as its amino acid sequences or physicochemical properties, despite the fact that peptides detected by MS are dependent on many factors, including protein sample preparation, digestion, separation, ionization, and precursor selection during MS experiments. DbyDeep (Detectability by Deep learning) is an innovative end-to-end LSTM network model for peptide detectability prediction that incorporates sequence contexts of peptides and their cleavage sites (by protease). Utilizing the cleavage site contexts could improve the performance of prediction, and DbyDeep outperformed existing methods in predicting peptides recognizable from multiple MS/MS data sets with diverse species and MS instruments. We argue for the necessity of a learning model that encompasses several contexts associated with peptide detection, as opposed to depending just on peptide sequences. There is a Python implementation of DbyDeep at https://github.com/BISCodeRepo/DbyDeep. |
format | Online Article Text |
id | pubmed-10401496 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-104014962023-08-05 DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning Son, Juho Na, Seungjin Paek, Eunok Anal Chem [Image: see text] Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the peptide itself, such as its amino acid sequences or physicochemical properties, despite the fact that peptides detected by MS are dependent on many factors, including protein sample preparation, digestion, separation, ionization, and precursor selection during MS experiments. DbyDeep (Detectability by Deep learning) is an innovative end-to-end LSTM network model for peptide detectability prediction that incorporates sequence contexts of peptides and their cleavage sites (by protease). Utilizing the cleavage site contexts could improve the performance of prediction, and DbyDeep outperformed existing methods in predicting peptides recognizable from multiple MS/MS data sets with diverse species and MS instruments. We argue for the necessity of a learning model that encompasses several contexts associated with peptide detection, as opposed to depending just on peptide sequences. There is a Python implementation of DbyDeep at https://github.com/BISCodeRepo/DbyDeep. American Chemical Society 2023-07-17 /pmc/articles/PMC10401496/ /pubmed/37459568 http://dx.doi.org/10.1021/acs.analchem.3c00460 Text en © 2023 American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Son, Juho Na, Seungjin Paek, Eunok DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning |
title | DbyDeep: Exploration
of MS-Detectable Peptides via
Deep Learning |
title_full | DbyDeep: Exploration
of MS-Detectable Peptides via
Deep Learning |
title_fullStr | DbyDeep: Exploration
of MS-Detectable Peptides via
Deep Learning |
title_full_unstemmed | DbyDeep: Exploration
of MS-Detectable Peptides via
Deep Learning |
title_short | DbyDeep: Exploration
of MS-Detectable Peptides via
Deep Learning |
title_sort | dbydeep: exploration
of ms-detectable peptides via
deep learning |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401496/ https://www.ncbi.nlm.nih.gov/pubmed/37459568 http://dx.doi.org/10.1021/acs.analchem.3c00460 |
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