| Autor: |
Francois Charih, James R. Green, Kyle K. Biggar |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
STAR Protocols, Vol 1, Iss 3, Pp 100135- (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2666-1667 |
| DOI: |
10.1016/j.xpro.2020.100135 |
| Popis: |
Summary: 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). |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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