34 research outputs found
Towards designing of a potential new HIV-1 protease inhibitor using QSAR study in combination with Molecular docking and Molecular dynamics simulations.
Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model's applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski's rule. Molecular Docking performed on Wild Type, and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes' stability allowing a comparative study on the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of darunavir and maybe used for further experimental studies. Our study may also be used as pipeline to search and design new potential inhibitors of HIV-1 proteases
Correlation of observed and predicted activities (training set in blue and test set in red).
Correlation of observed and predicted activities (training set in blue and test set in red).</p
Graphs representing the Radius of gyration (Rg) values for MT and WT proteases without as with ligands (ND and DRV) during the period of simulation.
Graphs representing the Radius of gyration (Rg) values for MT and WT proteases without as with ligands (ND and DRV) during the period of simulation.</p
Negative logarithm values of the biological activity concerning the 33 compounds.
Negative logarithm values of the biological activity concerning the 33 compounds.</p
2D-binding interactions in the active site of the wild type protease (WT-Darunavir).
2D-binding interactions in the active site of the wild type protease (WT-Darunavir).</p
Williams plot of standardized residual versus leverage for the MLR model (with: h* = 0.45 and residual limits = ± 2.5); training samples are designed in black color and test samples in red color.
Williams plot of standardized residual versus leverage for the MLR model (with: h* = 0.45 and residual limits = ± 2.5); training samples are designed in black color and test samples in red color.</p
Supporting information contains all the supporting tables and figures.
Supporting information contains all the supporting tables and figures.</p
The root means square fluctuation (RMSF) plots of MT (chain (A) and chain (B)) and WT (chain (A) and chain (B)) proteases without and with ligands (ND and DRV) during the period of simulation.
The root means square fluctuation (RMSF) plots of MT (chain (A) and chain (B)) and WT (chain (A) and chain (B)) proteases without and with ligands (ND and DRV) during the period of simulation.</p
Chemical structure of the new proposed drug (C<sub>27</sub>H<sub>32</sub>N<sub>6</sub>O<sub>4</sub>S).
Chemical structure of the new proposed drug (C27H32N6O4S).</p
3D-binding interactions in the active site of the wild type protease (WT-ND).
3D-binding interactions in the active site of the wild type protease (WT-ND).</p