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Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome.
Charih, Francois; Green, James R; Biggar, Kyle K.
Afiliação
  • Charih F; Institute of Biochemistry and Department of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada.
  • Green JR; Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5BS, Canada.
  • Biggar KK; Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5BS, Canada.
STAR Protoc ; 1(3): 100135, 2020 12 18.
Article em En | MEDLINE | ID: mdl-33377029
ABSTRACT
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).
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Espectrometria de Massas / Análise de Sequência de Proteína / Previsões Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: STAR Protoc Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Canadá

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Espectrometria de Massas / Análise de Sequência de Proteína / Previsões Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: STAR Protoc Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Canadá