Structure modeling and docking study of HCV NS5B-3a RNA polymerase for the identification of potent inhibitors

Phylogenetic analysis, structure modeling and docking study of HCV NS3 protease for the identification of potent inhibitors

The nonstructural protein 3 (NS3) helicase of HCV is believed to be a plausible target for the identification and designing of potent antiviral drugs. NS3 protein is involved in a positive sense single-stranded viral replication as well as it also cleaves viral poly protein into diverse mature proteins at different time spans. Structural exploration of NS3 revealed that HCV helicase could also act as translocase. In order to identify potential inhibitors for HCV-3a, the current study has been designed. Serum samples from the Pakistani HCV positive patients were collected, sequenced and after purification included in the present study. Phylogenetic analysis on the samples clustered around it in the same group with those from India. Using homology modeling technique, we determined 3D structure of NS3 gene of HCV-3a and employed further in docking studies to discover potent inhibitor against it. As a result of docking Compound 1, with IC50 value of 0.015 and -14.4 kcal/mol energy, ranked as a most pungent inhibitor among all the studied inhibitors. Compound 1 also exhibited good hydrogen bond interactions with the modeled protein. The finding of present study could be used as a lead in future to design an effective dual inhibitor against HCV-3a.

Applications of computer-aided approaches in the development of hepatitis C antiviral agents

INTRODUCTION

Hepatitis C virus (HCV) is a global health problem that causes several chronic life-threatening liver diseases. The numbers of people affected by HCV are rising annually. Since 2011, the FDA has approved several anti-HCV drugs; while many other promising HCV drugs are currently in late clinical trials. Areas covered: This review discusses the applications of different computational approaches in HCV drug design. Expert opinion: Molecular docking and virtual screening approaches have emerged as a low-cost tool to screen large databases and identify potential small-molecule hits against HCV targets. Ligand-based approaches are useful for filtering-out compounds with rich physicochemical properties to inhibit HCV targets. Molecular dynamics (MD) remains a useful tool in optimizing the ligand-protein complexes and understand the ligand binding modes and drug resistance mechanisms in HCV. Despite their varied roles, the application of in-silico approaches in HCV drug design is still in its infancy. A more mature application should aim at modelling the whole HCV replicon in its active form and help to identify new effective druggable sites within the replicon system. With more technological advancements, the roles of computer-aided methods are only going to increase several folds in the development of next-generation HCV drugs.

Molecular docking and structure-based virtual screening studies of potential drug target, CAAX prenyl proteases, of Leishmania donovani

Targeting CAAX prenyl proteases of Leishmania donovani can be a good approach towards developing a drug molecule against Leishmaniasis. We have modeled the structure of CAAX prenyl protease I and II of L. donovani, using homology modeling approach. The structures were further validated using Ramachandran plot and ProSA. Active site prediction has shown difference in the amino acid residues present at the active site of CAAX prenyl protease I and CAAX prenyl protease II. The electrostatic potential surface of the CAAX prenyl protease I and II has revealed that CAAX prenyl protease I has more electropositive and electronegative potentials as compared CAAX prenyl protease II suggesting significant difference in their activity. Molecular docking with known bisubstrate analog inhibitors of protein farnesyl transferase and peptidyl (acyloxy) methyl ketones reveals significant binding of these molecules with CAAX prenyl protease I, but comparatively less binding with CAAX prenyl protease II. New and potent inhibitors were also found using structure-based virtual screening. The best docked compounds obtained from virtual screening were subjected to induced fit docking to get best docked configurations. Prediction of drug-like characteristics has revealed that the best docked compounds are in line with Lipinski's rule. Moreover, best docked protein-ligand complexes of CAAX prenyl protease I and II are found to be stable throughout 20 ns simulation. Overall, the study has identified potent drug molecules targeting CAAX prenyl protease I and II of L. donovani whose drug candidature can be verified further using biochemical and cellular studies.