Encoding mu-opioid receptor biased agonism with interaction fingerprints

Area of Science:

• Pharmacology and Molecular Biology
• Computational Chemistry
• Drug Discovery

Background:

• Opioids are effective painkillers but carry risks of adverse effects, necessitating careful medical supervision.
• G-protein biased agonists targeting the μ-opioid receptor (MOR) offer a potentially safer therapeutic profile compared to traditional, non-biased ligands.
• Understanding the molecular interactions of biased ligands is crucial for developing safer analgesics.

Purpose of the Study:

• To identify a characteristic protein-ligand interaction fingerprint for biased μ-opioid receptor (MOR) agonists.
• To develop and apply a virtual screening strategy for discovering novel biased MOR ligands.
• To contribute to the understanding of biased signaling pathways in GPCRs.

Main Methods:

• Extensive all-atom molecular dynamics simulations were performed on two biased ligands and one balanced reference molecule.
• A novel protein-ligand interaction fingerprint was derived from simulation data to characterize biased agonism.
• A large database of 68,740 GPCR-active ligands was virtually screened using the identified fingerprint.

Main Results:

• A distinct interaction fingerprint capable of characterizing biased ligands was successfully identified.
• Virtual screening of the ligand database yielded exemplary molecules exhibiting the predicted interaction pattern for biased agonism.
• The study demonstrates a viable computational approach for identifying biased MOR ligands.

Conclusions:

• The identified interaction fingerprint is a valuable tool for the discovery of biased MOR ligands.
• This work advances the understanding of MOR biased signaling and its potential for developing safer analgesics.
• Computational methods, including molecular dynamics and virtual screening, are effective in guiding the search for novel therapeutic agents with improved safety profiles.