In Silico Screening of New Derivatives as Inhibitors of Enoyl-[Acyl-Carrier-Protein] Reductase From Staphylococcus Aureus Via 2D-QSAR Analysis, Molecular Docking and ADME/Tox Prediction
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Abstract
Staphylococcus aureus is the most dangerous of all staphylococci and can be potentially fatal if it enters the bloodstream, lungs or heart. In order to develop natural inhibitors of Staphylococcus aureus, a quantitative structure-activity relationship (2D-QSAR) study was carried out on a series of chrysin derivatives. 2D-QSAR models were developed using multiple linear regression (MLR) and artificial neural networks (ANN). Descriptors were selected using principal component analysis (PCA). Molecular docking was performed using Autodock Tools, Autodock vina and discovery studio, and the structure of Enoyl-[acyl carrier protein] reductase (FabI) from Staphylococcus aureus (4FS3) was extracted from the Protein Data Bank (PDB). The predicted of absorption, distribution, metabolism, excretion and toxicity (ADME/Tox properties) were determined using pkCSM and SwissADME and compared with those of the most active molecule in the original database. This study showed that there is a significant correlation between activity and three descriptors (atomic contribution to logP (GCUT_SLOGP_2), number of hydrogen bond acceptor atoms (a_acc) and value of diameter - radius / radius (petitjeanSC)). The RLM model has a correlation coefficient of R2 =0.78, root mean square error (RMSE=0.07) and R2test=0.85 and R2=0.96 for the ANN model. We then designed new derivatives with improved in silico activity. Molecular docking showed that they fit perfectly into the active pocket of the receptor with low binding energy (-10.3 Kcal/mol). The predicted ADME/Tox properties of the compounds show that they have satisfactory predicted properties (non-toxic and have no carcinogenic potential), which confirms that the new derivatives have interesting anti-Staphylococcus aureus activity.
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