A QSAR Study of Peptidyl Vinyl Sulfone Cysteine Protease Inhibitors Using Topomer CoMFA and Molecular Docking

Abstract

Background: Malaria is one of the most important infectious diseases in the world. The most severe form of malaria in humans is caused by Plasmodium falciparum. Malaria is a worldwide health problem, with 214 million new cases in 2015 and 438,000 deaths, most of which are in Africa. Therefore, there is an urgent need for novel, low-toxic, more specific inhibitors to find new antimalarial agents. A promising target for antimalarial drug design is falcipain-2, a cysteine protease from P. falciparum that has received considerable attention due to its key role in the life cycle of the parasite. Materials and Methods: Three-dimensional quantitative structure-activity relationship (3D-QSAR) models of 39 peptidyl vinyl sulfone cysteine protease inhibitors was constructed using Topomer CoMFA. Topomer Search was employed to virtually screen lead-like compounds in the ZINC database. Molecular docking was employed to further explore the binding requirements between the ligands and the receptor protein which included several hydrogen bonds between peptidyl vinyl sulfone cysteine protease inhibitors and active site residues. Results: The non-cross correlation coefficient (r2), the interaction validation coefficient (q2) and the external validation (r2 pred) were 0.902, 0.685 and 0.763, respectively. The results showed that the model not only had good estimation stability but also good prediction capability. 22 new molecules were obtained, whose predicted activity is higher than the template molecules. The results showed that the Topomer Search technology can be effectively applied to screen and design new peptidyl vinyl sulfone cysteine protease inhibitors. Molecular docking showed extensive interactions between peptidyl vinyl sulfone cysteine protease inhibitors and residues of LYS24, ASP21, LYS59, and ASP17 in the active site. Conclusion: : 39 peptidyl vinyl sulfone cysteine protease inhibitors were used in the 3D-QSAR study. Topomer CoMFA 3D-QSAR method was used to build the model, and the model was well predicted and statistically validated. The design of potent new inhibitors of cysteine protease can get useful insights from these results.