A computational approach for designing novel SARS-CoV-2 Mpro inhibitors: combined QSAR, molecular docking, and molecular dynamics simulation techniques

Abstract

The normal expression of the main protease (M^pro) plays a vital role in the life cycle of coronavirus. Highly active inhibitors could inhibit the normal circulation of the main protease to achieve therapeutic effects as anti-coronavirus agents. In the present research, 48 peptide compounds with SARS-CoV M^pro inhibition selected from the literature were used to establish robust Topomer CoMFA (q² = 0.743, r² = 0.938, and r_pred² = 0.700) and HQSAR (q² = 0.774, r² = 0.955, and r_pred² = 0.723) models. Structural modification information was used for designing new M^pro inhibitors. The high contribution-value descriptor generated by Topomer CoMFA was used to screen for the fragments that possess significant inhibitory activities from the ZINC drug database, and 24 new compounds with predicted high inhibitory activity at nanomolar concentration were designed by combining the high contribution value fragments. The molecular docking results further justified that these potential inhibitors could form hydrogen bonds with the residues of CYS145, GLN189, GLU166, HIS163, and GLY143 of target M^pro, which well explains their strong inhibitory effects. The molecular dynamics simulation results indicated that four highly active compounds could stably bond with SARS-CoV-2 M^pro and might be promising anti-SARS-CoV-2 M^pro candidates. Finally, all the newly designed compounds showed premium ADMET properties as per the predictions by the server in the public domain. This research work not only provides robust QSAR models as valuable screening tools for future anti-coronavirus drug development but also renders the newly designed SARS-CoV-2 M^pro inhibitors with activity at nanomolar concentration, which can be used for further characterization to obtain novel anti-coronavirus drugs for both SARS-CoV and SARS-CoV-2.