Intracellular pocket conformations determine signaling efficacy through the opioid receptor

Area of Science:

• Pharmacology and Molecular Biology
• Computational Chemistry and Cheminformatics

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.
• Activation of 7 transmembrane receptors (7TMRs) involves ligand-induced conformational shifts in intracellular pockets.

Purpose of the Study:

• To test the hypothesis that signaling through the mu opioid receptor is proportional to intracellular pocket conformation probabilities.
• To develop a machine learning model for accurately calculating signaling efficacy.
• To identify structural features associated with receptor activation and functional selectivity.

Main Methods:

• Developed a machine learning model based on the hypothesis of linear proportionality between signaling and intracellular pocket conformation probabilities.
• Utilized spectroscopic data of receptor-ligand complexes.
• Validated the model's accuracy in calculating G protein and beta-arrestin-2 signaling efficacies.

Main Results:

• The machine learning model accurately predicted signaling efficacy for both G protein and beta-arrestin-2 pathways.
• Key structural features associated with activation include intracellular pocket expansion, toggle switch rotation, and sodium binding pocket collapse.
• Distinct pathway activation correlates with specific arrangements of ligand/sodium binding pockets and the intracellular pocket.

Conclusions:

• Signaling efficacy of 7TMRs can be accurately computed using a machine learning approach based on conformational probabilities.
• This method moves beyond simple active/inactive ligand categorization to provide quantitative prediction of multi-pathway signaling.
• Understanding the interplay of structural features offers insights into ligand-induced functional selectivity.