In silico design of selective high affinity ligands against HCV using novel computational diology tools - Al Olabi, Reem Rafik (AUC Knowledge Fountain, 2011)
Background: Hepatitis C virus (HCV) infects 170 million patients worldwide. The absence of an effective mean of treatment or prophylaxis makes HCV infection a serious public health problem. Generating selective high affinity ligands (SHALs) against HCV could provide a solution to this public health burden. HCV E2 glycoprotein is required for HCV entry into host cells. It binds to CD81, a host receptor protein that belongs to the tetraspanin family and plays a critical role in viral invasion. HCV protease is essential for the cleavage of non structural proteins, in addition to preventing the phosphorylation of human interferon regulatory factor 3 and thus prevents the anti-viral response. Objective: To design a cocktail of SHAL-based inhibitors against several target proteins such as CD81, HCV E2, and HCV protease and optimize the currently available E2 homology models. Methods and findings: Different homology modeling techniques such as AS2TS, Phyre, ROSETTA and TASSER, were used to obtain reliable models of HCV NS3 serine protease and HCV E2 glycoprotein. LGA was used to structurally analyze the models in addition of clustering the obtained models and finding the closest structural templates using StralCP. Auto Dock Tools 1.5.6 was used to prepare the crystal structures of the CD81-LEL protein (1G8Q and 1IV5), HCV protease, HCV polymerase and the homology model of HCV E2 by deleting water molecules, adding polar hydrogens, and assigning Gasteiger charges and to create a grid bounding box, which provided the desired grid parameter file using 0.375 A spacing. Autoligand, an AutoDock tool, was used to identify several binding sites on the protein targets. Fill points were created using a 1 A grid, and the calculations were performed using 10 to 210 fill points. AutoDock 4.2 was used to screen 30,000 ligands obtained from different libraries (NCI_DSII, Sigma and Asinex) and identify small molecules that might bind to each site. The docking results were analyzed and the top 20 ligands for each binding site on the target proteins were ranked according to selection criteria required for the design of promising SHALs. Distances between pairs of bound ligands were estimated and used to design several SHALs that should bind selectively to the target proteins. Conclusion: New computational tools have been used to design in silico several SHAL-based inhibitors that might have the potential to prevent both HCV entry into hepatocytes and the production of inflammatory cytokines that accelerate liver damage when targeting E2-CD81 interaction. It might alter protein processing and viral replication if HCV protease was targeted. By targeting HCV RNA dependent RNA polymerase, the HCV replication could be blocked. In addition, blocking the complexation of NS3 with the NS4A co-factor will render it non functional and thus block the replication process and disrupt the HCV life cycle. If a reliable E2 homology model was developed based on different studies and validations, it could help generate selective high affinity ligands that block the earliest phase of the HCV life cycle.