Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Charih, Francois, Green, James R., Biggar, Kyle K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
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
_version_ 1783626719635177472
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
work_keys_str_mv AT charihfrancois usingmachinelearningandtargetedmassspectrometrytoexplorethemethyllysproteome
AT greenjamesr usingmachinelearningandtargetedmassspectrometrytoexplorethemethyllysproteome
AT biggarkylek usingmachinelearningandtargetedmassspectrometrytoexplorethemethyllysproteome