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Structure-Based Approach for the Prediction of Mu-opioid Binding Affinity of Unclassified Designer Fentanyl-Like Molecules.

ABSTRACT: Three quantitative structure-activity relationship (QSAR) models for predicting the affinity of mu-opioid receptor (OR) ligands have been developed. The resulted models, exploiting the accessibility of the QSAR modeling, generate a useful tool for the investigation and identification of unclassified fentanyl-like structures. The models have been built using a set of 115 molecules using Forge as a software, and the quality was confirmed by statistical analysis, resulting in being effective for their predictive and descriptive capabilities. The three different approaches were then combined to produce a consensus model and were exploited to explore the chemical landscape of 3000 fentanyl-like structures, generated by a theoretical scaffold-hopping approach. The findings of this study should facilitate the identification and classification of new OR ligands with fentanyl-like structures.

SUBMITTER: Floresta G

PROVIDER: S-EPMC6539757 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Structure-Based Approach for the Prediction of Mu-opioid Binding Affinity of Unclassified Designer Fentanyl-Like Molecules.

Floresta Giuseppe G Rescifina Antonio A Abbate Vincenzo V

International journal of molecular sciences 20190510 9

Three quantitative structure-activity relationship (QSAR) models for predicting the affinity of mu-opioid receptor (OR) ligands have been developed. The resulted models, exploiting the accessibility of the QSAR modeling, generate a useful tool for the investigation and identification of unclassified fentanyl-like structures. The models have been built using a set of 115 molecules using Forge as a software, and the quality was confirmed by statistical analysis, resulting in being effective for th ...[more]

PMID: 31083294

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Project description: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).

Dataset Information

Structure-Based Approach for the Prediction of Mu-opioid Binding Affinity of Unclassified Designer Fentanyl-Like Molecules.

Publications

Structure-Based Approach for the Prediction of Mu-opioid Binding Affinity of Unclassified Designer Fentanyl-Like Molecules.

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