Biased signaling occurs when a ligand preferentially activates a specific signaling pathway at a certain receptor within a particular cellular environment. As pharmaceuticals, functionally selective (biased) compounds can engage beneficial signaling pathways while avoiding those linked to adverse effects, resulting in safer and targeted therapeutics. However, existing experimental methods for characterizing ligand bias are often time-consuming and costly, and they hardly elucidate the structural basis of biased signaling. Here, we present microswitch-guided sampling (MGS), a computational method that uses conformational changes in molecular switches to assess ligand bias in G protein-coupled receptor systems. Using initial 2 s all-atom molecular dynamics simulations of the {beta}1-adrenergic receptor ({beta}1AR), the chemokine receptor CXCR3, and the -opioid receptor (OR) in complex with either G protein or {beta}-arrestin and known biased ligands, we identified microswitches whose conformational changes were linked to activation of a specific signaling pathway. We then sampled simulation trajectories for representative complex conformations selected at these microswitch-change time points and used them as starting points for 500 ns ligand-swapped simulations, in which we exchanged biased ligands between complexes. We observed clear differences in conformational changes between complexes bound to pathway-activating and non-activating ligands in at least two of three replicas across all examined systems, demonstrating that MGS can distinguish G protein-biased from {beta}-arrestin-biased ligands. These findings show that MGS is an effective tool for assessing signaling pathway bias in GPCR systems and suggest that this methodology can be applied to other GPCR targets to act as a fast, ns-scale computational assay to detect functional selectivity with molecular dynamics.
Dragan, P., Latek, D.
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