Hepatitis C Virus (HCV) infection causes around three quarters of worldwide chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. Interferon and ribavirin combination therapy is not effective with all HCV genotypes, and has serious side effects that may result in discontinuation of treatment. The aim of our work is to design inhibitors against HCV envelope glycoprotein E2 to prevent its binding to CD81 surface receptor and the consequent viral entry into hepatocytes. Currently, no crystal structure is constructed for E2. However, a model of E2 is available, Yagnik et al. model, which served as the starting point for the structure-based inhibitor discovery experiment. We detected four strictly conserved and 20 highly conserved regions across 66 E2 sequences representing seven HCV genotypes. Then, the conserved regions were screened in the Protein Data Bank to find highly matching 3D structures and compare them to the corresponding substructures of the model to increase trust in those regions. The docking algorithm, AutoDock Vina, was tested by docking Johns Hopkins Clinical Compound Library ligands on the three identified E2-heparin binding sites on the model. Then, AMBER99 force field was used to refine the binding energies after Vina screening. Heparin was found to score more than the library compounds, but many compounds scored similar to heparin, further proposing them for experimental investigation. The two E2-CD81 binding sites were targeted on the model using those ligands, where AMBER99 was also used to prioritize the library compounds for experimental testing. In conclusion, we propose a two-step strategy for docking, which includes using Vina to remove poor binding modes then using AMBER99 to refine those binding modes. We have identified 24 strictly and highly conserved regions in HCV E2 protein. Top binding ligands in Johns Hopkins Clinical Compound Library that bind to CD81 binding sites on the E2 protein have been identified. Future investigations will include experimentally testing the top scoring ligands to identify HCV drug candidates against E2-CD81 interaction.
School of Sciences and Engineering
MS in Biotechnology
Azzazy, Hassan M. E.
Committee Member 1
Kocher, Jean-Pierre A.
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Danial, S. A.
(2021).E2 evolutionary motif prediction and identification of drug candidates to prevent hepatitis c virus cellular entry [Thesis, the American University in Cairo]. AUC Knowledge Fountain.
Danial, Shairy Azmy. E2 evolutionary motif prediction and identification of drug candidates to prevent hepatitis c virus cellular entry. 2021. American University in Cairo, Thesis. AUC Knowledge Fountain.