Encoding mu-opioid receptor biased agonism with interaction fingerprints

• Published in: Journal of computer-aided molecular design Vol. 35; no. 11; pp. 1081 - 1093
• Main Authors: Hernández-Alvarado, R. Bruno, Madariaga-Mazón, Abraham, Cosme-Vela, Fernando, Marmolejo-Valencia, Andrés F., Nefzi, Adel, Martinez-Mayorga, Karina
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-11-2021; Springer Nature B.V
• Subjects: Algorithms; Analgesics; Analgesics, Opioid - pharmacology; Animal Anatomy; Chemistry; Chemistry and Materials Science; Computer Applications in Chemistry; Fingerprints; GTP-Binding Proteins - metabolism; Histology; Ligands; Molecular Docking Simulation; Molecular dynamics; Molecular Dynamics Simulation; Morphology; Narcotics; Physical Chemistry; Protein Binding; Proteins; Receptors; Receptors, Opioid, mu - agonists; Receptors, Opioid, mu - metabolism; Signal Transduction - drug effects; Virtual screening; Protein–ligand interaction fingerprint; Herkinorin; Mu-opioid receptor; Biased factor; Biased agonism
• ISSN: 0920-654X; 1573-4951
• DOI: 10.1007/s10822-021-00422-5

Opioids are potent painkillers, however, their therapeutic use requires close medical monitoring to diminish the risk of severe adverse effects. The G-protein biased agonists of the μ-opioid receptor (MOR) have shown safer therapeutic profiles than non-biased ligands. In this work, we performed extensive all-atom molecular dynamics simulations of two markedly biased ligands and a balanced reference molecule. From those simulations, we identified a protein–ligand interaction fingerprint that characterizes biased ligands. Then, we built and virtually screened a database containing 68,740 ligands with proven or potential GPCR agonistic activity. Exemplary molecules that fulfill the interacting pattern for biased agonism are showcased, illustrating the usefulness of this work for the search of biased MOR ligands and how this contributes to the understanding of MOR biased signaling.