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. (December 2018)

• Record Type: Journal Article
• Authors: Wang, Mingxing; Wang, Ying; Kong, Dejiang; Jiang, Hailun; Wang, Jian; Cheng, Maosheng
• Is Part Of: Computational biology and chemistry. Volume 77(2018)
• Page Start: 214
• Page End: 225
• Publication Date: 2018-12
• Subjects: Nav1.7 -- Pain -- Molecular docking -- 3D-QSAR -- Molecular dynamics simulations; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.10.009
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: 3D-QSAR, molecular docking and molecular dynamics studies were performed on a range of aryl sulfonamides as Nav1.7 inhibitors. CoMFA and CoMSIA contour maps provided relationships between structure and activity. Molecular docking revealed the binding characteristics between Nav1.7 and the inhibitor. Molecular dynamics simulations and binding free energy calculations indicated key amino acid residues and major interactions at the active site. 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.