A computational approach for identification and development of novel inhibitors targeting viral polymerases

Positive strand RNA viruses, which include hepatitis C virus (HCV), human immunodeficiency virus (HIV, and Bovine Viral Diarrhea Virus (BVDV), are known to create havoc for humans and animal health alike. Although vaccines have helped to control several of the most important viral pathogens, there is currently little prospect of an effective vaccine for either HCV or HIV. These pathogens infect ~170 million and ~40 million people worldwide, respectively, hastening the need for effective antiviral drugs. Likewise BVDV infects domesticated livestock causing significant economic losses worldwide. The development of new, effective antiviral compounds for combating these debilitating human (HIV and HCV) and animal pathogen (BVDV) is therefore of paramount importance, and is the focus of this thesis. Herein, polymerases of three positive strand RNA viruses, viz HCV, BVDV and HIV have been targeted with the goal of improving the efficacy of antivirals against wide range of resistant mutations. Lack of effective therapies for these viral infections as most of the established treatments are not always effective or well tolerated, highlights an urgent need for further refinement and development of antiviral drugs. It is not only the specific need that has inspired this work but also the idea to test and develop protocols that might enable a more rational structurebased drug design to be performed by keeping a tradeoff among rapidity, accuracy, and efficacy. Traditional methods for general drug discovery typically include evaluating random compound libraries for activity in relevant cellfree or cellbased assays. Success in antiviral development has emerged from the discovery of more focused libraries that provide clues about structureactivity relationships. Combining these with more recent approaches including structural biology and computational modeling can work efficiently to hasten discovery of active molecules. The ability to design drugs interfering with the progression of infection of virus comes with i)the knowledge of pathological, cellular and molecular mechanism involved in the disease; and ii)the identification of macromolecule (i.e possible drug target) involved in pathological pathways, their 3D structures and their functions. The biological activity of drug molecules is dependent on the threedimensional arrangement of its functional groups, which specifically bind to their target. Consequently, the structural information of the target protein is essential in drug development. Proteins are dynamic molecules and often undergo conformational change upon ligand binding. The flexible loop regions and in general the flexibility of the structure have a critical functional role in enzymes, but those features and their connection with the functionality of protein are hard to retrieve from xray, NMR techniques and cryoEM techniques. Being aware of the importance of the relationship structurefunction and structureactivity at large, i.e., including dynamics and interactions with solvent, in our work we are trying to address some of the relevant problems of drug development; basic key determinants in proteinligand stability, mechanism of inhibition, why and how, flexibility and collective motion of the protein is essential part in improvement of rational drug design, how mutation renders the protein resistant again potent drugs; the effect of resistance mutation on the flexibility and stability of protein, what is the mechanism of drug resistance, change in energetics consequences, affecting the conformation in wild and mutant systems. Various biophysical techniques of the computational arsenal we have applied have provided huge wealth of information related to protein dynamics and proteinligand recognition. These methods have grown in their effectiveness not only by offering a deeper understanding of the basic science, the biological events and molecular interactions that define a target for therapeutic intervention, but also because of advances in algorithms, representations, and mathematical procedures for studying such processes. This work represents the application of several computational techniques, such as docking, molecular dynamics, algorithms to calculate free energy of binding of ligands into the binding pocket (ex MMPBSA) and algorithms to study rare events (for ex. binding and unbinding of ligand from the binding site, Metadynamics) 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 docking and undocking path and to calculate binding energetics. 4