الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
HCV is a global health threat that is one of the major causes of cirrhosis, hepatic failure and hepatocellular carcinoma. The single stranded RNA virus has eleven proteins. Five of them are structural (core protein p21, core protein, p19, envelope glycoprotein E1, envelope glycoprotein E2, and core protein p7 ion channel) and six are non-structural (protease NS2– 3, serine protease NS3, non-structural protein 4A, non-structural protein 4B, non-structural protein 5A, and RNA-dependent RNA polymerase). Targeting these proteins had proven a great success in treating the virus. Two of the most crucial proteins in the life cycle are the NS5B polymerase and the NS3/4A protease. Targeting these two proteins lead to the discovery of many direct acting antivirals (DAAs) including the pan-genotypic polymerase inhibitor Sofosbuvir and the protease inhibitors telaprevir, boceprevir, simeprevir and grazoprevir. Despite the great success of these drug in treating HCV genotype 1, they suffered from severe activity reduction when it comes to other genotypes. HCV genotype 4a dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS5B and NS3 of genotype 4a using homology modeling, PLIF (protein–ligand interaction fingerprint), docking, pharmacophore, 3D-QSAR and dynamic simulation. In chapter2, aiming to develop specific potent NS5B polymerase inhibitors for HCV GT4a, A high-quality 3D model of HCV NS5B polymerase of genotype 4a was constructed using crystal structure of HCV NS5B polymerase of genotype 1 (PDB ID: 3hkw) as a template. This model shown very acceptable quality based on the result of the RAMACHANDRAN plot, Verify 3D and ERRAT. PLIF was generated using five crystal structures of HCV NS5B (PDB ID: 4mia, 4mib, 4mk9, 4mka, and 4mkb). Which revealed the most important residues in the polymerase palm site and their interactions with the co-crystalized ligands. A 3D pharmacophore model consisting of three features (four components) was developed from the generated PLIF data. The generated pharmacophore had a good predictive capability with EF = 90, GH = 0.9 and a perfect ROC curve. Then the pharmacophore was used as a screening filter for drug- like zinc database (17000328 compounds). Only 900 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. A 3D CoMFA QSAR was developed using 42 compound as a training and 19 compound as a test set. The QSAR model had very acceptable criteria with q2 = 0.685, R2ncv = 0.949, SEE = 0.124, F = 65.6, R2pred = 0.893, SEP = 0.198. The good predictive criteria of the 3D CoMFA QSAR enabled us to design and screen some potential inhibitors, these compounds were further evaluated by the docking stage. The highest ranked five hits from docking result (compounds (p1–p4) and compound q1 were selected for further analysis. They exhibited stronger interaction and higher binding affinity.