Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis and molecular docking-based combined in silico rational approach to design potent and novel TRPV1 antagonists

Abstract

The discovery of clinically relevant antagonists of TRPV1 for neuropathy pain therapy has proven to be a challenging task. For better understanding of the molecular interactions of antagonists with TRPV1 receptor, a series of chroman and tetrahydroquinoline ureas were analyzed by k-nearest neighbor molecular field analysis (kNN-MFA) and molecular docking. To elucidate the structural properties required for activity as TRPV1 antagonists, we report here kNN-MFA-based 3D-QSAR model for chroman and tetrahydroquinoline ureas as potent TRPV1 antagonists. Sphere exclusion method was used for dividing the compounds into training (26 compounds) and test (5 compounds) set. Overall model classification accuracy was 81.35 % (q2 = 0.8135, representing internal validation) in training set and 81.44 % (pred_r2 = 0.8144, representing external validation) in test set using stepwise forward as a method of variable selection. The stereo view of molecular rectangular grid field of 3D-QSAR using this approach showed that steric and hydrophobic effects dominantly determine binding affinities. Furthermore, the crystal structure of TRPV1 was obtained from protein data bank (PDB code 2NYJ, resolution 3.20 A), and docking of 31 TRPV1 antagonists into putative binding sites of the TRPV1 were studies. Molecular docking was employed to explore the binding mode between these compounds and the receptor, as well as help understanding the structure–activity relationship revealed by kNN-MFA. Our QSAR model and molecular docking results corroborate with each other and propose directions for the design of new antagonists with better activity toward TRPV1.