Ebola virus disease remains one of the most serious viral infections with no approved small-molecule treatments. The Ebola virus glycoprotein (EBOV-GP), which enables the virus's entry to host cells, is a promising target for drug discovery. In this study, a multistage computer-aided drug discovery approach was used to identify new specific EBOV-GP inhibitors. A reliable QSAR model was built using 55 terpenoid derivatives. This model was able to predict the activity of newly designed compounds with good accuracy and validated statistical metrics (Rtr2 = 0.70; Rext2 = 0.73). It was subsequently applied to screen over 15,500 newly generated compounds from three lead molecules by fragment-based design tools. Predicted activity, binding affinity toward EBOV-GP, and good ADMET drug-like properties prioritized the eleven most promising hits. Through 150 ns molecular dynamics simulations, these compounds remained stable in the EBOV-GP binding site. Further binding free energy analysis (MM/PBSA) showed strong binding affinities, especially for the compounds , , , and . This study showed how combining QSAR, fragment-based design, docking, ADMET, and molecular dynamics could help in identifying potent and safe small molecules against the EBOV-GP. The top compounds are ready for further experimental and in vitro biological testing.
Keywords: EBOV-GP, Ebola virus, MD simulation, MM/PBSA, QSAR, fragment-based drug design, molecular docking, monoterpenoids
International journal of molecular sciences
Journal Article
English
41977174
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