To probe interaction of morphine and IBNtxA with 7TM and 6TM variants of the human μ-opioid receptor using all-atom molecular dynamics simulations with an explicit membrane

2018 ◽  
Vol 20 (3) ◽  
pp. 1724-1741 ◽  
Author(s):  
Safaa Sader ◽  
Kumar Anant ◽  
Chun Wu
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Opioid Receptor ◽  
Safety Profile ◽  
Μ Opioid Receptor ◽  
Dynamics Simulations

IBNtxA, a morphine derivative, is 10-fold more potent and has a better safety profile than morphine.

Homology Modeling and Molecular Dynamics Simulations of the Mu Opioid Receptor in a Membrane-Aqueous System

ChemBioChem ◽  
2005 ◽  
Vol 6 (5) ◽  
pp. 853-859 ◽  
Author(s):  
Yan Zhang ◽  
Yuk Y. Sham ◽  
Ramkumar Rajamani ◽  
Jiali Gao ◽  
Philip S. Portoghese
Keyword(s):  
Molecular Dynamics ◽  
Homology Modeling ◽  
Molecular Dynamics Simulations ◽  
Opioid Receptor ◽  
Mu Opioid Receptor ◽  
Aqueous System ◽  
Mu Opioid ◽  
Dynamics Simulations

scholarly journals Molecular Dynamics Simulations to Investigate How PZM21 Affects the Conformational State of the μ-Opioid Receptor Upon Activation

2020 ◽  
Vol 21 (13) ◽  
pp. 4699 ◽  
Author(s):  
Zhennan Zhao ◽  
Tingting Huang ◽  
Jiazhong Li
Keyword(s):  
Molecular Dynamics ◽  
Side Effects ◽  
G Protein ◽  
Opioid Receptor ◽  
Conformational Changes ◽  
Opioid Analgesics ◽  
Dynamics Simulation ◽  
Analgesic Effects ◽  
Μ Opioid Receptor ◽  
Dynamics Simulations

Opioid analgesics such as morphine have indispensable roles in analgesia. However, morphine use can elicit side effects such as respiratory depression and constipation. It has been reported that G protein-biased agonists as substitutes for classic opioid agonists can alleviate (or even eliminate) these side effects. The compounds PZM21 and TRV130 could be such alternatives. Nevertheless, there are controversies regarding the efficacy and G protein-biased ability of PZM21. To demonstrate a rationale for the reduced biasing agonism of PZM21 compared with that of TRV130 at the molecular level, we undertook a long-term molecular dynamics simulation of the μ-opioid receptor (MOR) upon the binding of three ligands: morphine, TRV130, and PZM21. We found that the delayed movement of the W2936.48 (Ballesteros–Weinstein numbering) side chain was a factor determining the dose-dependent agonism of PZM21. Differences in conformational changes of W3187.35, Y3267.43, and Y3367.53 in PZM21 and TRV130 explained the observed differences in bias between these ligands. The extent of water movements across the receptor channel was correlated with analgesic effects. Taken together, these data suggest that the observed differences in conformational changes of the studied MOR–ligand complexes point to the low-potency and lower bias effects of PZM21 compared with the other two ligands, and they lay the foundation for the development of G protein-biased agonists.

scholarly journals Fentanyl Family at the Mu-Opioid Receptor: Uniform Assessment of Binding and Computational Analysis

Molecules ◽  
2019 ◽  
Vol 24 (4) ◽  
pp. 740 ◽  
Author(s):  
Piotr Lipiński ◽  
Piotr Kosson ◽  
Joanna Matalińska ◽  
Piotr Roszkowski ◽  
Zbigniew Czarnocki ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Binding Affinity ◽  
Opioid Receptor ◽  
Medium Size ◽  
Scoring Functions ◽  
Binding Modes ◽  
Ionic Interaction ◽  
Competitive Binding Assay ◽  
Μ Opioid Receptor ◽  
Dynamics Simulations

Interactions of 21 fentanyl derivatives with μ-opioid receptor (μOR) were studied using experimental and theoretical methods. Their binding to μOR was assessed with radioligand competitive binding assay. A uniform set of binding affinity data contains values for two novel and one previously uncharacterized derivative. The data confirms trends known so far and thanks to their uniformity, they facilitate further comparisons. In order to provide structural hypotheses explaining the experimental affinities, the complexes of the studied derivatives with μOR were modeled and subject to molecular dynamics simulations. Five common General Features (GFs) of fentanyls’ binding modes stemmed from these simulations. They include: GF1) the ionic interaction between D147 and the ligands’ piperidine NH+ moiety; GF2) the N-chain orientation towards the μOR interior; GF3) the other pole of ligands is directed towards the receptor outlet; GF4) the aromatic anilide ring penetrates the subpocket formed by TM3, TM4, ECL1 and ECL2; GF5) the 4-axial substituent (if present) is directed towards W318. Except for the ionic interaction with D147, the majority of fentanyl-μOR contacts is hydrophobic. Interestingly, it was possible to find nonlinear relationships between the binding affinity and the volume of the N-chain and/or anilide’s aromatic ring. This kind of relationships is consistent with the apolar character of interactions involved in ligand–receptor binding. The affinity reaches the optimum for medium size while it decreases for both large and small substituents. Additionally, a linear correlation between the volumes and the average dihedral angles of W293 and W133 was revealed by the molecular dynamics study. This seems particularly important, as the W293 residue is involved in the activation processes. Further, the Y326 (OH) and D147 (Cγ) distance found in the simulations also depends on the ligands’ size. In contrast, neither RMSF measures nor D114/Y336 hydrations show significant structure-based correlations. They also do not differentiate studied fentanyl derivatives. Eventually, none of 14 popular scoring functions yielded a significant correlation between the predicted and observed affinity data (R < 0.30, n = 28).

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