Identification and development of novel Inhibitors of viral polymerases: An In-silico Approach

Our ability to answer some of the most fundamental questions of drug development process, such as how proteins undergo conformational changes to bind a ligand, what are key protein residues which play pivotal role for the binding, why a compound is active or inactive, or how its activity might be improved, how protein dynamics is changed on introduction of resistant mutations, how resistant mutations render protein resistant against inhibitors, what the mechanism of resistance changes in the binding free energy of a particular drug candidate or the mechanisms and energetic consequences of conformational changes in a protein etc, is limited often by our inability to appropriately catch dynamic features, including interactions with the solvent, in a crystallographic structure which is just an average structure and thus lacks dynamics. Here is where various biophysics based computational techniques come to our rescue, which provide huge wealth of information related to protein dynamics and proteinligand recognition. These methods have grown in their effectiveness because of advances in algorithms, representations, and mathematical procedures for studying such processes and are in the position of providing improved understanding of the basic science, the biological events and molecular interactions that define a target for therapeutic intervention. However, the approximations introduced in the computational approaches at different levels make a single technique in many cases inappropriate to tackle the dauntingly complex problems related to the drug design. Thus, a continuous feedback with experiments is required but also the appropriate combination of different computational strategies might be helpful. This work represents the application of computational techniques, such as docking, molecular dynamics, algorithms to calculate free energy of binding of ligands into the binding pocket ( MMPBSA) and algorithms to study rare events (like unbinding of ligand from the binding site, Metadynamics, etc.) to explore, at microscopic level, the key pattern of interaction between protein and ligand, to understand the effect of mutations, to get an insight of the full and undocking path and to calculate binding energetics. In this thesis, polymerases of three positive strand RNA viruses, viz, Hepatitis C Virus (HCV), Bovine Viral Diarrhea Virus (BVDV) and Human Immunodeficiency Virus (HIV) have been targeted with an aim to come out with more potent antivirals effective against wide range of resistant mutations. These viruses have created havoc by effecting Human race directly or indirectly and thereby creating an urgent need for further refinement and new development of antivirals drugs. Herein, plethora of computational techniques has been applied on the three protein systems to answer fundamental questions of drug resistance, drug inhibition and providing clues for improvement of antivirals.