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Transition Intermediates Encode Ligand Efficacy at the μ-Opioid Receptor-Gi Protein Complex

How ligands with different efficacies regulate G protein-coupled receptor signaling remains incompletely understood. Here, we combine Markov state modeling of nearly one millisecond of aggregate all-atom molecular dynamics simulations with time-resolved cryogenic-electron microscopy (cryo-EM) to reconstruct the most probable transition pathways connecting recently resolved structures of guanosine…

Ligands with varying degrees of effectiveness impact G protein-coupled receptor signaling in a complex manner, a process not yet fully understood. Researchers have now utilized a combination of Markov state modeling and time-resolved cryogenic-electron microscopy (cryo-EM) to unveil the probable transition pathways connecting specific structures of guanosine triphosphate (GTP)-bound mu-opioid receptor (MOR)-Gi1 complexes with the full agonist lofentanil (LFT) or the partial agonist mitragynine pseudoindoxyl (MP).

This investigation has led to the prediction of four previously unknown intermediate conformations. By following these predictions, the reanalysis of the cryo-EM particle ensemble has confirmed the presence of conformational populations corresponding to all four intermediates. The most significant structural disparities arising due to ligand differences are observed within these intermediates, rather than in the previously identified states.

LFT demonstrates a primarily sequential and kinetically efficient transition pathway, while MP stabilizes a wider range of intermediates along this pathway, thereby slowing the progression towards later stages. Collectively, these combined methods offer a more comprehensive thermodynamic and kinetic description of MOR-mediated G protein activation, highlighting the role of opioid efficacy in the differential stabilization and kinetics of transition intermediates beyond GTP binding but prior to complete G protein dissociation.

Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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