Computational Modeling of Macrocycles and Structure-Based Design of Novel Antibacterial Compounds

Abstract: The integration of computational methods into the drug discovery process provides valuable tools to help advance new and improved drugs into the clinic. As medicinal chemists explore novel targets and new areas of chemical space, our computational toolkit must also evolve.Macrocycles are high-value scaffolds in medicinal chemistry due to their attractive physiochemical properties and intricate interactions with biomolecules. However, given the significant challenges associated with their synthesis, improved computational tools are required to both understand macrocycle conformation and binding preferences and to also efficiently guide medicinal chemistry efforts. Therefore, this thesis focuses on the evaluation, optimization and application of computational methods for macrocycle drug discovery.Our initial work, Paper I, investigated both rigid and flexible macrocycle docking techniques. This showed that rigid docking of conformational ensembles generated using a range of sampling methods could result in significant differences in docking accuracy. Furthermore, we showed that either rigid docking of MD/LLMOD generated conformers or flexible docking could be applied.In Paper II, we conducted further investigations of macrocycle conformational sampling by comparing more general sampling methods to those specialized towards macrocyclic scaffolds. The study showed that the general conformational sampling methods perform well compared with the more specialized methods. Our work also shows that the general methods can themselves be modified for improved macrocycle sampling.Building on these findings, Paper III compares the conformational preferences of linear ligands and their closely related macrocyclic analogs. Interestingly, our analysis showed that for many of the macrocyclic ensembles they were not significantly more focused towards the bioactive conformation than their linear analogs.In Paper IV, our computational toolkit was used to design novel antibacterial macrocycles targeting signal peptidase I. Here we developed macrocyclic compounds with nanomolar inhibitory activity against signal peptidase I, which also showed antibacterial activity.