Bevirimat Dimeglumine

Maturation inhibitors (MIs) efficiently block HIV-1 maturation by inhibiting the cleavage of the capsid protein and spacer peptide 1 (CA-SP1) leading to the production of immature and non-infectious virus particles. We have previously reported that second-generation MIs were more potent than bevirimat (BVM) against HIV-1 subtype C. In-silico studies on interaction of with BVM and their analogs have been limited to HIV-1 subtype B(5I4T) due to lack of an available 3D structure for HIV-1 subtype C virus. In our current study, we have developed a 3D model of HIV-1C Gag CA-SP1 region using protein homology modeling with HIV-1 subtype B(514T) as a template. The HIV-1 C homology model generated was extensively validated using several online tools and served as a template to perform molecular docking studies with eight well-characterized MIs. The docked complex of HIV-1C and all nine MIs was subjected to molecular dynamics simulation for 100 ns using AMBER and binding free energy calculations were done using MM-GBSA. Based on our data, CV8611 exhibited highest binding energy of -6.5 Kcal/mol among all BVM analogs. CV8611 formed strong interactions with Gly222 and Met235 of HIV-1C Gag CA-SP1 during MD simulation and remained intact. The root mean square deviation and root mean square fluctuation values of the complex were stable during the simulations. Our study is the first to report construction and validation of 3D model for the HIV-1C Gag CA-SP1, which could serve as a crucial tool in the structure-aided design of novel and broadly acting maturation inhibitors.Communicated by Ramaswamy H. Sarma.

Computational screening is one of the fundamental techniques in drug discovery. Each compound in a chemical database is bound to the target protein in virtual, and candidate compounds are selected from the binding scores. In this work, we carried out combinational computation of docking simulation to generate binding poses and molecular mechanics calculation to estimate binding scores. The coronavirus infectious disease has spread worldwide, and effective chemotherapy is strongly required. The viral 3-chymotrypsin-like (3CL) protease is a good target of low molecular-weight inhibitors. Hence, computational screening was performed to search for inhibitory compounds acting on the 3CL protease. As a preliminary assessment of the performance of this approach, we used 51 compounds for which inhibitory activity had already been confirmed. Docking simulations and molecular mechanics calculations were performed to evaluate binding scores. The preliminary evaluation suggested that our approach successfully selected the inhibitory compounds identified by the experiments. The same approach was applied to 8820 compounds in a database consisting of approved and investigational chemicals. Hence, docking simulations, molecular mechanics calculations, and re-evaluation of binding scores including solvation effects were performed, and the top 200 poses were selected as candidates for experimental assays. Consequently, 25 compounds were chosen for in vitro measurement of the enzymatic inhibitory activity. From the enzymatic assay, 5 compounds were identified to have inhibitory activities against the 3CL protease. The present work demonstrated the feasibility of a combination of docking simulation and molecular mechanics calculation for practical use in computational virtual screening.