NeuropharmacologyBiophysicsPeer-reviewed journal article
A Putative Binding Model of Nitazene Derivatives at the mu-Opioid Receptor
Joseph ClaytoniD, Lei ShiiD, Michael J. RobertsoniD, Georgios SkiniotisiD, Michael MichaelidesiD, Lidiya StavitskayaiD, Jana SheniD
Abstract
Nitazenes are a class of novel synthetic opioids with exceptionally high potency. Currently, an experimental structure of {micro}OR-opioid receptor ({micro}OR) in complex with a nitazene is lacking. Here we used a suite of computational tools, including consensus docking, conventional molecular dynamics (MD) and metadynamics simulations, to investigate the {micro}OR binding modes of nitro-containing meto-, eto-, proto-, buto-, and isotonitazenes and nitro-less analogs, metodes-, etodes-, and protodesnitazenes. Docking generated three binding modes, whereby the nitro-substituted or unsubstituted benzimidazole group extends into SP1 (subpocket 1 between transmembrane helix or TM 2 and 3), SP2 (subpocket 2 between TM1, TM2, and TM7) or SP3 (subpocket 3 between TM5 and TM6). Simulations suggest that etonitazene and likely also other nitazenes favor the SP2-binding mode. Comparison to the experimental structures of {micro}OR in complex with BU72, fentanyl, and mitragynine pseudoindoxyl (MP) allows us to propose a putative model for {micro}OR-ligand recognition in which ligand can access hydrophobic SP1 or hydrophilic SP2, mediated by the conformational change of Gln1242.60. Interestingly, in addition to water-mediated hydrogen bonds, the nitro group in nitazenes forms a{pi} -hole interaction with the conserved Tyr751.39. Our computational analysis provides new insights into the mechanism of {micro}OR-opioid recognition, paving the way for investigations of the structure-activity relationships of nitazenes. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/616560v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@f3a933org.highwire.dtl.DTLVardef@e98f41org.highwire.dtl.DTLVardef@1bc0962org.highwire.dtl.DTLVardef@c416b_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: structural biology
- research focus: protein structure prediction
- approach: deep learning
- approach: statistical model
- Citations
- 12
- FWCI
- 2.98
- Field percentile
- top 8%
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