Molecular modelling of HIV-1 protease inhibition: A drug design for HIV/AIDS

Abstract

In this study, computer aided drug design (CADD) procedures were employed as a tool to discover potential drug candidates for HIV-1 protease. The use of these methods has proven to be useful over the years, with some recent drugs in the market being discovered through these computational tools. CADD saved time and minimized costs in discovering suitable HIV inhibitors. Using two approaches of finding a drug candidate, a wild type of HIV was used as a target to determine the binding affinity of these drugs. In the first approach, the ZINC database was screened using the Lipinski's rule of five. A pharmacophore was then constructed using the co-crystallised structure of 1rv7 with lopinavir and was used to screen all the compounds that complied with the Lipinski's rule of five. All compounds with RMSD less than 0.5 Å were identified and docked against 1rv7. Subsequently, in the second approach phytochemicals from five plants were studied, whereby a library of these compounds was prepared on the Molecular Operating Environment (MOE)software. A pharmacophore model was also constructed based on the features of the best three compounds obtained from the ZINC database. This pharmacophore was used to screen all the phytochemical prepared in the library. Compounds from the ZINC database were docked against 1rv7 and all the plant extracts were docked in 1rv7. The docked compounds were analysed based on the following interactions: Van der Waals, conventional hydrogen bonds, pi-Sigma, alkyl, pi-alkyl. The best three compounds were selected based on the SwissADMED/T properties, to evaluate the adsorption, distribution, metabolism, excretion and toxicity characteristic profiling. This included the analysis of the following models, iLOGP, XLOGP, MLOGP, WLOGP and SILICOS-IT. Zinc_001456687980 and Zinc_000015276352 with a binding affinity of (-8.0 KD) and (-6.2 KD) respectively, were the two lead compounds from the ZINC database. Based on molecular dynamics imulation analysis, Epicatechin (-8 KD), Juglanin (-7.6 KD) and Catechin (-6.9 KD) were the three lead phytochemical. The trajectories for all compounds were saved at every 100ps, with simulation time of 200ns. The root mean square fluctuation, the root mean square deviation and the proteinligand contact calculation, and protein-ligand contacts were calculated from the MD trajectories.The IC50 (nM) values for Zinc_000015276352, Zinc_001456687980, Juglanin, Epicatechin, Catechin and Dolutegravir were 2.046(nM), 1,573(nM), 1.954(nM), 0.979(nM), 1.042(nM) and1,435 (nM) respectively.