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Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome
Protein lysine methylation mediates a variety of biological processes, and their dysregulation has been established to play pivotal roles in human disease. A number of these sites constitute attractive drug targets. However, systematic identification of methylation sites is challenging and resource...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757287/ https://www.ncbi.nlm.nih.gov/pubmed/33377029 http://dx.doi.org/10.1016/j.xpro.2020.100135 |
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author | Charih, Francois Green, James R. Biggar, Kyle K. |
author_facet | Charih, Francois Green, James R. Biggar, Kyle K. |
author_sort | Charih, Francois |
collection | PubMed |
description | Protein lysine methylation mediates a variety of biological processes, and their dysregulation has been established to play pivotal roles in human disease. A number of these sites constitute attractive drug targets. However, systematic identification of methylation sites is challenging and resource intensive. Here, we present a protocol combining MethylSight, a machine learning model trained to identify promising lysine methylation sites, and mass spectrometry for subsequent validation. Our approach can reduce the time and investment required to identify novel methylation sites. For complete information on the use and execution of this protocol, please refer to Biggar et al. (2020). |
format | Online Article Text |
id | pubmed-7757287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-77572872020-12-28 Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome Charih, Francois Green, James R. Biggar, Kyle K. STAR Protoc Protocol Protein lysine methylation mediates a variety of biological processes, and their dysregulation has been established to play pivotal roles in human disease. A number of these sites constitute attractive drug targets. However, systematic identification of methylation sites is challenging and resource intensive. Here, we present a protocol combining MethylSight, a machine learning model trained to identify promising lysine methylation sites, and mass spectrometry for subsequent validation. Our approach can reduce the time and investment required to identify novel methylation sites. For complete information on the use and execution of this protocol, please refer to Biggar et al. (2020). Elsevier 2020-10-21 /pmc/articles/PMC7757287/ /pubmed/33377029 http://dx.doi.org/10.1016/j.xpro.2020.100135 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Charih, Francois Green, James R. Biggar, Kyle K. Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome |
title | Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome |
title_full | Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome |
title_fullStr | Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome |
title_full_unstemmed | Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome |
title_short | Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome |
title_sort | using machine learning and targeted mass spectrometry to explore the methyl-lys proteome |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757287/ https://www.ncbi.nlm.nih.gov/pubmed/33377029 http://dx.doi.org/10.1016/j.xpro.2020.100135 |
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