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

## TL;DR

The study shows that the shape of receptor pockets determines how strongly a drug activates cell signals through the μ opioid receptor.

## Contribution

A machine learning model is introduced that calculates signaling efficacy based on intracellular pocket conformations.

## Key findings

- Signaling efficacy is linearly proportional to the probability of intracellular pocket conformations.
- The model accurately computes G protein and β-arrestin-2 signaling efficacy.
- Intracellular pocket expansion and sodium binding pocket collapse are linked to receptor activation.

## Abstract

It has been challenging to determine how a ligand that
binds to
a receptor activates downstream signaling pathways and to predict
the strength of signaling. The challenge is compounded by functional
selectivity, in which a single ligand binding to a single receptor
can activate multiple signaling pathways at different levels. Spectroscopic
studies show that in the largest class of cell surface receptors,
7 transmembrane receptors (7TMRs), activation is associated with ligand-induced
shifts in the equilibria of intracellular pocket conformations in
the absence of transducer proteins. We hypothesized that signaling
through the μ opioid receptor, a prototypical 7TMR, is linearly
proportional to the equilibrium probability of observing intracellular
pocket conformations in the receptor–ligand complex. Here,
we show that a machine learning model based on this hypothesis accurately
calculates the efficacy of both G protein and β-arrestin-2 signaling.
Structural features that the model associates with activation are
intracellular pocket expansion, toggle switch rotation, and sodium
binding pocket collapse. Distinct pathways are activated by different
arrangements of the ligand and sodium binding pockets and the intracellular
pocket. While recent work has categorized ligands as active or inactive
(or partially active) based on binding affinities to two conformations,
our approach accurately computes signaling efficacy along multiple
pathways.