Deciphering molecular properties and docking studies of hepatitis C and non-hepatitis C antiviral inhibitors - A computational approach

Computational Drug Design against Ebola Virus Targeting Viral Matrix Protein VP30

Ebola viral disease (EVD) is a deadly infectious hemorrhagic viral fever caused by the Ebola virus with a high mortality rate. Until date, there is no effective drug or vaccination available to combat this condition. This study focuses on designing an effective antiviral drug for Ebola viral disease targeting viral protein 30 (VP30) of Ebola virus, highly required for transcription initiation. The lead molecules were screened for Lipinski rule of five, ADMET study following which molecular docking and bioactivity prediction was carried out. The compounds with the least binding energy were analyzed using interaction software. The results revealed that 6-Hydroxyluteolin and (-)-Arctigenin represent active lead compounds that inhibit the activity of VP30 protein and exhibits efficient pharmacokinetics. Both these compounds are plant-derived flavonoids and possess no known adverse effects on human health. In addition, they bind strongly to the predicted binding site centered on Lys180, suggesting that these two lead molecules can be imperative in designing a potential drug for EVD.

The activity prediction of indole inhibitors against HCV NS5B polymerase

Non-structural viral protein 5B (NS5B) is a viral protein in hepatitis C virus. Although various inhibitors against NS5B have been found, the activity prediction of similar untested inhibitors is still highly desirable. In this respect, the Tchebichef moments (TMs) calculated from the images of molecular structures were regarded as the independent variables while the inhibitory activity (pIC₅₀ ) was the dependent variable, and the predictive model was established by means of stepwise regression. The R-squared of leave-one-out cross-validation (Q² ) for the training set and the R-squared of prediction ( R p 2 ) for external independent test set were 0.919 and 0.927, respectively. The obtained model was also evaluated strictly. Compared with the multivariate curve resolution with alternating least squares (MCR-ALS) and the QSAR approaches derived from the literature, the proposed method is more accurate and reliable. This study not only provides an effective approach to predict the biological activity of RNA replication's inhibitors, but also extends the QSAR modeling technique.

Structure-based drug design of novel peptidomimetic cellulose derivatives as HCV-NS3 protease inhibitors

Hepatitis C Virus (HCV) represents a global health threat not only due to the large number of reported worldwide HCV infections, but also due to the absence of a reliable vaccine for its prevention. HCV NS3 protease is one of the most important targets for drug design aiming at the deactivation of HCV. In the present work, molecular docking simulations are carried out for suggested novel NS3 protease inhibitors applied to the Egyptian genotype 4. These inhibitors are modifications of dimer cellulose by adding a hexa-peptide to the cellulose at one of the positions 2, 3, 6, 2', 3' or 6'. Results show that the inhibitor compound with the hexa-peptide at position 6 shows significantly higher simulation docking score with HCV NS3 protease active site. This is supported by low total energy value of docking system, formation of two H-bonds with HCV NS3 protease active site residues, high binding affinity and increased stability in the interaction system.