Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition

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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Bibliographic Details
Main Authors: Joseph Bemister-Buffington, Alex J. Wolf, Sebastian Raschka, Leslie A. Kuhn
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Biomolecules
Subjects:
gpcr activity determinants
flexibility analysis
coupled residues
allostery
proflex
mlxtend
feature selection
pattern classification
Online Access:https://www.mdpi.com/2218-273X/10/3/454

Internet

https://www.mdpi.com/2218-273X/10/3/454

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