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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 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.

Here, we focused on exploring the selectivity mechanism against Nav1.7 over Nav1.4 due to different binding modes of two selected inhibitors. By the superposition of Nav1.7 and Nav1.4 proteins, we selected the most homologous chain of Nav1.7 with Nav1.4, defining the active site of Nav1.4-VSD4 based on the aryl sulfonamide binding site of Nav1.7-VSD4. Comparison of the conformations exhibited by Tyr1386 (Nav1.4) and Tyr1537 (Nav1.7) suggested that the steric hindrance caused by Tyr1386 owned primary influence on inhibition selectivity, which was further verified through molecular docking and MD simulation of two representative inhibitors. Our finding would be helpful for discovery of selective Nav1.7 inhibitors over Nav1.4.