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Article Dans Une Revue Journal of Chemical Information and Modeling Année : 2019

Binding of Sulfoxaflor to Aplysia californica -AChBP: Computational Insights from Multiscale Approaches

Résumé

Structural features and binding properties of sulfoxaflor (SFX) with Ac-AChBP, the surrogate of the insect nAChR ligand binding domain (LBD), are reported herein using various complementary molecular modeling approaches (QM, molecular docking, molecular dynamics, and QM/QM′). The different SFX stereoisomers show distinct behaviors in terms of binding and interactions with Ac-AChBP. Molecular docking and Molecular Dynamics (MD) simulations highlight the specific intermolecular contacts involved in the binding of the different SFX isomers and the relative contribution of the SFX functional groups. QM/QM′ calculations provide further insights and a significant refinement of the geometric and energetic contributions of the various residues leading to a preference for the SS and RR stereoisomers. Notable differences in terms of binding interactions are pointed out for the four stereoisomers. The results point out the induced fit of the Ac-AChBP binding site according to the SFX stereoisomer. In this process, the water molecules-mediated contacts play a key role, their energetic contribution being among the most important for the various stereoisomers. In all cases, the interaction with Trp147 is the major binding component, through CH···π and π···π interactions. This study provides a rationale for the binding of SFX to insect nAChR, in particular with respect to the new class of sulfoximine-based insect nAChR competitive modulators, and points out the requirements of various levels of theory for an accurate description of ligand–receptor interactions.
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Dates et versions

hal-02314990 , version 1 (14-10-2019)

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Zakaria Alamiddine, Balaji Selvam, Jerome Graton, Adèle Laurent, Elodie Landagaray, et al.. Binding of Sulfoxaflor to Aplysia californica -AChBP: Computational Insights from Multiscale Approaches. Journal of Chemical Information and Modeling, 2019, 59 (9), pp.3755-3769. ⟨10.1021/acs.jcim.9b00272⟩. ⟨hal-02314990⟩
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