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In silico exploration of aryl sulfonamide analogs as voltage-gated sodium channel 1.7 inhibitors by using 3D-QSAR, molecular docking study, and molecular dynamics simulations

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It has been demonstrated by human genetics that the voltage-gated sodium channel Nav1.7 is currently a promising target for the treatment of pain. In this research, we performed molecular simulation… Click to show full abstract

It has been demonstrated by human genetics that the voltage-gated sodium channel Nav1.7 is currently a promising target for the treatment of pain. In this research, we performed molecular simulation works on a series of classic aryl sulfonamide Nav1.7 inhibitors using three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking and molecular dynamics (MD) simulations for the first time to explore the correlation between their structures and activities. The results of the relevant statistical parameters of comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analyses (CoMSIA) had been verified to be reasonable, and the deep relationship between the structures and activities of these inhibitors was obtained by analyzing the contour maps. The generated 3D-QSAR model showed a good predictive ability and provided valuable clues for the rational modification of molecules. The interactions between compounds and proteins were modeled by molecular docking studies. Finally, accuracy of the docking results and stability of the complexes were verified by 100 ns MD simulations. Detailed information on the key residues at the binding site and the types of interactions they participate in involved was obtained. The van der Waals energy contributed the most in the molecular binding process according to the calculation of binding free energy. All research results provided a good basis for further research on novel and effective Nav1.7 inhibitors.

Keywords: aryl sulfonamide; sodium channel; inhibitors using; gated sodium; molecular docking; voltage gated

Journal Title: Computational biology and chemistry
Year Published: 2018

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