In silico evaluation of atazanavir as a potential HIV main protease inhibitor and its comparison with new designed analogs.

Starting three decades ago and spreading rapidly around the world, acquired immunodeficiency syndrome (AIDS) is an infectious disease distinct from other contagious diseases by its unique ways of transmission. Over the past few decades, research into new drug compounds has been accompanied by extensive advances, and the design and manufacture of drugs that inhibit virus enzymes is one way to combat the AIDS virus. Since blocking enzyme activity can kill a pathogen or correct a metabolic imbalance, the design and use of enzyme inhibitors is a new approach against viruses. We carried out an in-depth analysis of the efficacy of atazanavir and its newly designed analogs as human immunodeficiency virus (HIV) protease inhibitors using molecular docking. The best-designed analogs were then compared with atazanavir by the molecular dynamics simulation. The most promising results were ultimately found based on the docking analysis for HIV protease. Several exhibited an estimated free binding energy lower than -9.45 kcal/mol, indicating better prediction results than the atazanavir. ATV7 inhibitor with antiviral action may be more beneficial for infected patients with HIV. Molecular dynamics analysis and binding energy also showed that the ATV7 drug had more inhibitory ability than the atazanavir drug.

Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease.

The novel coronavirus (SARS-CoV-2) has infected several million people and caused thousands of deaths worldwide since December 2019. As the disease is spreading rapidly all over the world, it is urgent to find effective drugs to treat the virus. The main protease (Mpro) of SARS-CoV-2 is one of the potential drug targets. Therefore, in this context, we used rigorous computational methods, including molecular docking, fast pulling of ligand (FPL), and free energy perturbation (FEP), to investigate potential inhibitors of SARS-CoV-2 Mpro. We first tested our approach with three reported inhibitors of SARS-CoV-2 Mpro, and our computational results are in good agreement with the respective experimental data. Subsequently, we applied our approach on a database of ∼4600 natural compounds, as well as 8 available HIV-1 protease (PR) inhibitors and an aza-peptide epoxide. Molecular docking resulted in a short list of 35 natural compounds, which was subsequently refined using the FPL scheme. FPL simulations resulted in five potential inhibitors, including three natural compounds and two available HIV-1 PR inhibitors. Finally, FEP, the most accurate and precise method, was used to determine the absolute binding free energy of these five compounds. FEP results indicate that two natural compounds, cannabisin A and isoacteoside, and an HIV-1 PR inhibitor, darunavir, exhibit a large binding free energy to SARS-CoV-2 Mpro, which is larger than that of 13b, the most reliable SARS-CoV-2 Mpro inhibitor recently reported. The binding free energy largely arises from van der Waals interaction. We also found that Glu166 forms H-bonds to all of the inhibitors. Replacing Glu166 by an alanine residue leads to ∼2.0 kcal/mol decreases in the affinity of darunavir to SARS-CoV-2 Mpro. Our results could contribute to the development of potential drugs inhibiting SARS-CoV-2.

Drug binding dynamics of the dimeric SARS-CoV-2 main protease, determined by molecular dynamics simulation.

We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein-ligand complexes and suggest the possibilities of further drug optimisations.