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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2020
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| Materias: | |
| 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 |