QSAR, molecular docking, molecular dynamics and MM-GBSA approach for identification of prospective benzotriazole-based SARS-CoV 3CL protease inhibitors

Abstract

The 3CL Protease of severe acute respiratory syndrome coronavirus (SARS-CoV), responsible for viral replication, has emerged as an essential target for designing anti-coronaviral inhibitors in drug discovery. In recent years, small molecule and peptidomimetic inhibitors have been used to target the inhibition of SARS-CoV 3CL Protease. In this study, we have developed 2D and 3D Quantitative structure activity relationship (QSAR) models on 3CL protease inhibitors with good predictive capability to propose inhibitors with improved affinities. Based on the 3 D contour maps, three new inhibitors were designed in silico, which were further subjected to molecular docking to explore their binding modes. The newly designed compounds showed improved interaction energies toward SARS-CoV-3CLPro due to additional interactions with the active site residues. The molecular docking studies of the most potent compounds revealed specific interactions with Glu 166 and Cys 145. Furthermore, absorption, distribution, metabolism, elimination (ADME) and drug-likeness evaluation revealed improved pharmacokinetic properties for these compounds. The molecular dynamics simulations confirmed the stability of the interactions identified by docking. The results presented would guide the development of new 3CL protease inhibitors with improved affinities in the future. Communicated by Ramaswamy H. Sarma