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Development and application of molecular modeling methods for characterization of drug targets in Mycobacterium tuberculosis

Abstract

For decades, tuberculosis (TB) has persisted as a global health burden with over a million people succumbing to the disease each year. As cases of multi-drug resistant and extensively drug-resistant increase, there is an urgent need for the development of novel therapeutics to combat the disease. Fortunately, advances in protein structure determination and development of computational modeling tools provide valuable insight into the specific interactions the mediate binding between proteins and ligands. Nonetheless, the results of these methods are sometimes ambiguous or inconclusive. Taken together, structural determination and computational modeling can complement each other to provide more robust and interpretable data. In Chapter 2 of this work, I focus on the development of a computational method to enhance sampling of side chains, which can rearrange in the presence of different ligands. Specifically, I describe the development and validation of a non-equilibrium candidate Monte Carlo method for the enhanced sampling of side chain rotamers during molecular dynamics (MD) simulations. I validate this method on a simple valine-alanine dipeptide and demonstrate that it significantly improves rotamer sampling of a buried valine sidechain in the binding site of model system T4 lysozyme L99A. In Chapters 3 and 4, we use X-ray crystallography and MD to elucidate and study the structures of two potential TB drug targets from Mycobacterium tuberculosis (Mtb): Mtb heme oxygenase (MhuD) and Mtb malic enzyme (MEZ). In the structure of MhuD in complex with product, we observe the formation of a novel α-helix; however, the unusual 5:2 ratio of product to protein subunit in the asymmetric unit, as well as the proximity of the helix to the crystallographic interface, provoke questions regarding its biological relevance. Using MD, I confirm that formation of the α-helix is favored in the presence of product and likely associated with product-turnover. In Chapter 3, I describe the X-ray structure of apo-MEZ along with results from differential scanning fluorimetry and gel filtration. MD simulations provide insight into interactions of MEZ with NAD(P)+, Mn2+, and malate, while also corroborating our empirical observations that MEZ is unusually disordered.

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