bioRxivBiochemistry
Structural and molecular dynamics of Mycobacterium tuberculosis malic enzyme, a potential anti-TB drug target
K. H. Burley, B. J. Cuthbert, P. Basu, J. Newcombe, E. M. Irimpan, R. Quechol, I. P. Foik, D. L. Mobley, D. J. Beste, C. W. Goulding
Abstract
Tuberculosis (TB) is the most lethal bacterial infectious disease worldwide. It is notoriously difficult to treat, requiring a cocktail of antibiotics administered over many months. The dense, waxy outer membrane of the TB-causing agent, Mycobacterium tuberculosis (Mtb), acts as a formidable barrier against uptake of antibiotics. Subsequently, enzymes involved in maintaining the integrity of the Mtb cell wall are promising drug targets. Recently, we demonstrated that Mtb lacking malic enzyme (MEZ) has altered cell wall lipid composition and attenuated uptake by macrophages. These results suggest that MEZ provides the required reducing power for lipid biosynthesis. Here, we present the X-ray crystal structure of MEZ to 3.6 [A] resolution and compare it with known structures of prokaryotic and eukaryotic malic enzymes. We use biochemical assays to determine its oligomeric state and to evaluate the effects of pH and allosteric regulators on its kinetics and thermal stability. To assess the interactions between MEZ and its substrate malate and cofactors, Mn2+ and NAD(P)+, we ran a series of molecular dynamics (MD) simulations. First, the MD analysis corroborates our empirical observations that MEZ is unusually disordered, which persists even with the addition of substrate and cofactors. Second, the MD simulations reveal that MEZ subunits alternate between open and closed states and that MEZ can stably bind its NAD(P)+ cofactor in multiple conformations, including an inactive, compact NAD+ form. Together the structure of MEZ and insights from its dynamics can be harnessed to inform the design of MEZ inhibitors that target Mtb.
- organism: bacteria (other)
- assay: structural biology
- research focus: protein structure prediction
- approach: experimental assay
- approach: statistical model
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