Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition

We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our analysis was performed on the helical segments and loo...

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Detalles Bibliográficos
Autores principales: Bemister-Buffington, Joseph, Wolf, Alex J., Raschka, Sebastian, Kuhn, Leslie A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175283/
https://www.ncbi.nlm.nih.gov/pubmed/32183371
http://dx.doi.org/10.3390/biom10030454

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175283/
https://www.ncbi.nlm.nih.gov/pubmed/32183371
http://dx.doi.org/10.3390/biom10030454

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