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Characterizing conformational states in GPCR structures using machine learning
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DOI：10.1038/s41598-023-47698-1.png)
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Abstract
En 中文
G protein-coupled receptors (GPCRs) play a pivotal role in signal transduction and represent attractive targets for drug development. Recent advances in structural biology have provided insights into GPCR conformational states, which are critical for understanding their signaling pathways and facilitating structure-based drug discovery. In this study, we introduce a machine learning approach for conformational state annotation of GPCRs. We represent GPCR conformations as high-dimensional feature vectors, incorporating information about amino acid residue pairs involved in the activation pathway. Using a dataset of GPCR conformations in inactive and active states obtained through molecular dynamics simulations, we trained machine learning models to distinguish between inactive-like and active-like conformations. The developed model provides interpretable predictions and can be used for the large-scale analysis of molecular dynamics trajectories of GPCRs.
Keywords:
PROTEIN-COUPLED RECEPTORS
BETA-GAMMA-SUBUNITS
DYNAMICS
ACTIVATION
MUTATIONS
TARGETS
BINDING
MODELS
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