Intracellular Pocket Conformations Determine Signaling Efficacy through the μ Opioid Receptor.

Area of Science:

• Pharmacology
• Biochemistry
• Computational Biology

Background:

• Determining how ligands activate downstream signaling pathways and predicting signaling strength is challenging.
• Functional selectivity, where one ligand-receptor pair activates multiple pathways, complicates predictions.
• Seven transmembrane receptors (7TMRs) activation involves ligand-induced conformational shifts in intracellular pockets.

Purpose of the Study:

• To hypothesize and test if signaling through the mu opioid receptor is proportional to intracellular pocket conformations.
• To develop a machine learning model for predicting signaling efficacy in 7TMRs.
• To identify structural features associated with receptor activation and functional selectivity.

Main Methods:

• Utilized spectroscopic studies to observe conformational changes in 7TMRs.
• Developed a machine learning model based on the hypothesis of linear proportionality between signaling and pocket conformations.
• Validated the model by accurately calculating G protein and beta-arrestin-2 signaling efficacies.

Main Results:

• The machine learning model accurately predicts signaling efficacy for both G protein and beta-arrestin-2 pathways.
• Identified key structural features associated with activation: intracellular pocket expansion, toggle switch rotation, and sodium binding pocket collapse.
• Demonstrated that distinct pathway activation depends on specific arrangements of ligand, sodium, and intracellular pockets.

Conclusions:

• Signaling efficacy in 7TMRs can be accurately computed using a machine learning model based on intracellular pocket conformations.
• The model provides a quantitative approach to understanding functional selectivity and predicting ligand efficacy.
• This approach moves beyond simple active/inactive ligand categorization to a more nuanced prediction of multi-pathway signaling strength.