Conventional screening of potential drug candidates through wet lab affinity experiments using libraries of thousands of modified molecules is time and resource consuming, along with the fact that it contributes to the widening time gap between the discovery of disease-causing mutations and the implementation of resulting novel treatments. It is necessary to explore whether the preliminary use of high-throughput virtual screening (HTVS) software such as PyRx will curb both the time and money spent in discovering novel treatments for diseases such as congenital heart defects (CHDs). For example, AXIN2, a protein involved in a negative feedback loop inhibiting the Wnt/β-catenin signaling pathway important for cardiogenesis, has recently been associated with CHD. The loss-of-function mutation L10F on the tankyrase-binding domain of AXIN2 has been shown to upregulate the pathway by loss of inhibition ability, leading to the accumulation of intracellular β-catenin. In a different paper, however, AXIN2 has been shown to be stabilized using XAV-939, a small-molecule drug which targets tankyrase. PyRx and VMD will be used to modify the drug in order to increase its binding affinity to AXIN2, stabilizing the protein and reinstating its inhibitory property to treat CHDs. When used in adjunction to wet lab experiments, HTVS software may decrease costs and the time required to bring a potentially life-saving treatment into use.

Philosophiae Doctor - PhD / Given the significant disease burden caused by human coronaviruses, the discovery of an effective antiviral strategy is paramount, however there is still no effective therapy to combat infection. This thesis details the in silica exploration of ligand libraries to identify candidate lead compounds that, based on multiple criteria, have a high probability of inhibiting the 3 chymotrypsin-like protease (3CUro) of human coronaviruses. Atomistic models of the 3CUro were obtained from the Protein Data Bank or theoretical models were successfully generated by homology modelling. These structures served the basis of both structure- and ligand-based drug design studies. Consensus molecular docking and pharmacophore modelling protocols were adapted to explore the ZINC Drugs-Now dataset in a high throughput virtual screening strategy to identify ligands which computationally bound to the active site of the 3CUro . Molecular dynamics was further utilized to confirm the binding mode and interactions observed in the static structure- and ligand-based techniques were correct via analysis of various parameters in a IOns simulation. Molecular docking and pharmacophore models identified a total of 19 ligands which displayed the potential to computationally bind to all 3CUro included in the study. Strategies employed to identify these lead compounds also indicated that a known inhibitor of the SARS-Co V 3CUro also has potential as a broad spectrum lead compound. Further analysis by molecular dynamic simulations largely confirmed the binding mode and ligand orientations identified by the former techniques. The comprehensive approach used in this study improves the probability of identifying experimental actives and represents a cost effective pipeline for the often expensive and time consuming process of lead discovery. These identified lead compounds represent an ideal starting point for assays to confirm in vitro activity, where experimentally confirmed actives will be proceeded to subsequent studies on lead optimization.