Encoding mu-opioid receptor biased agonism with interaction fingerprints

J Comput Aided Mol Des. 2021 Nov;35(11):1081-1093. doi: 10.1007/s10822-021-00422-5. Epub 2021 Oct 29.

Abstract

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.

Keywords: Biased agonism; Biased factor; Herkinorin; Mu-opioid receptor; Protein–ligand interaction fingerprint; Virtual screening.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Analgesics, Opioid / pharmacology
  • GTP-Binding Proteins / metabolism
  • Ligands
  • Molecular Docking Simulation
  • Molecular Dynamics Simulation
  • Protein Binding
  • Receptors, Opioid, mu / agonists*
  • Receptors, Opioid, mu / metabolism
  • Signal Transduction / drug effects

Substances

  • Analgesics, Opioid
  • Ligands
  • Receptors, Opioid, mu
  • GTP-Binding Proteins