Computational drug design applied to neglected disease

Herein is described the identification of novel inhibitors of key pathogenic proteins, the use of established computational techniques to further drug discovery, and the development of novel computational methods, with the ultimate goal of identifying small-molecule compounds that, with further development, may serve as future treatments for neglected diseases. Two of the pathogens targeted, Trypanosoma brucei and Trypanosoma cruzi, are neglected because they are tropical, and drugs marketed solely to the developing world are rarely profitable. A third disease, H1N1 pandemic influenza, is somewhat neglected because it is only now emergent. For Trypanosoma brucei, two drug-discovery projects are described, focusing on two crucial enzymes : UDP-galactose 4'-epimerase and RNA editing ligase 1. For Trypanosoma cruzi, the dynamics of cruzain, a crucial cysteine protease, are studied and characterized. Finally, for influenza, predicted inhibitors of neuraminidase (N1) are presented. Aside from describing identified enzyme inhibitors, the current work also describes several new methods that may be generally applicable to drug discovery. These new methods include a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein, a novel computer-aided drug design algorithm (AutoGrow) that combines the strengths of both fragment-based growing and docking algorithms, and an extension to the relaxed complex scheme that accounts for both population-shift and induced-fit contributions to ligand bindin