Kinetic modelling of GlmU reactions - prioritization of reaction for therapeutic application

Abstract

Mycobacterium tuberculosis(Mtu), a successful pathogen, has developed resistance against the existing anti-tubercular drugs necessitating discovery of drugs with novel action. Enzymes involved in peptidoglycan biosynthesis are attractive targets for antibacterial drug discovery. The bifunctional enzyme mycobacterial GlmU (Glucosamine 1-phosphate N-acetyltransferase/ N-acetylglucosamine-1-phosphate uridyltransferase) has been a target enzyme for drug discovery. Its C- and N- terminal domains catalyze acetyltransferase (rxn-1) and uridyltransferase (rxn-2) activities respectively and the final product is involved in peptidoglycan synthesis. However, the bifunctional nature of GlmU poses difficulty in deciding which function to be intervened for therapeutic advantage. Genetic analysis showed this as an essential gene but it is still unclear whether any one or both of the activities are critical for cell survival. Often enzymatic activity with suitable high-throughput assay is chosen for random screening, which may not be the appropriate biological function inhibited for maximal effect. Prediction of rate-limiting function by dynamic network analysis of reactions could be an option to identify the appropriate function. With a view to provide insights into biochemical assays with appropriate activity for inhibitor screening, kinetic modelling studies on GlmU were undertaken. Kinetic model of Mtu GlmU-catalyzed reactions was built based on the available kinetic data on Mtu and deduction from Escherichia coli data. Several model variants were constructed including coupled/decoupled, varying metabolite concentrations and presence/absence of product inhibitions. This study demonstrates that in coupled model at low metabolite concentrations, inhibition of either of the GlmU reactions cause significant decrement in the overall GlmU rate. However at higher metabolite concentrations, rxn-2 showed higher decrement. Moreover, with available intracellular concentration of the metabolites and in vivo variant of model, uncompetitive inhibition of rxn-2 caused highest decrement. Thus, at physiologically relevant metabolite concentrations, targeting uridyltranferase activity of Mtu GlmU would be a better choice for therapeutic intervention.

Figure 1. Pathway context of GlmU-catalyzed reactions.

Figure 2. Equilibria between enzyme species for ordered bi-bi mechanism of enzymatic reaction.

A, B  = First and second substrate of enzyme E; P, Q  = First and second substrates for the reverse reaction, their binding to enzyme accounts for product inhibition; I  = Different types of hypothetical inhibitor, whose type is determined by the form of enzyme it binds to: I binding to free E (forming E-I complex) is a competitive inhibitor with respect to A, I binding to E-A complex (forming E-A-I complex) is uncompetitive inhibitor with respect to A and I binding to E-A-B complex (forming E-A-B-I complex) is uncompetitive inhibitor with respect to both A and B; K_ic  = Inhibition constant of hypothetical competitive inhibitor; K_{iu_<metabolite>}  = Inhibition constant of hypothetical uncompetitive inhibitor where the inhibitor behaves uncompetitive against the metabolite indicated within <>.

Figure 3. Experimental vs. simulated concentration response curves.

GlcNAc1P concentration response curve; Curves obtained from experiment: Black; Curves obtained from simulation: Gray; v  = Velocity of GlmU rxn-2. Assays were carried out at 25°C in assay buffer containing 50 mM Hepes KOH pH 7.5, 5 mM MgCl2. 5 mM DTT, 0.3 units/ml pyrophosphatase and the phosphate formed was detected using malachite green reagent from Innova Biosciences. For GlcNAc1P KM determination UTP was fixed at 250 µM.

Figure 4. Dynamic behaviour of the rates of GlmU reactions in coupled vs. decoupled models.

Plots corresponding to medium ( = K_M) metabolite concentrations; v  = Rates of GlmU rxn-1 (broken lines) and rxn-2 (solid lines); Panel 1A: In vitro variant GlmU rxn-1; Panel 1B: In vitro variant GlmU rxn-2; Panel 2A: In vivo variant GlmU rxn-1; Panel 2B: In vivo variant GlmU rxn-2; Coupled model: Black lines; Decoupled model: Gray lines.

Figure 5. Effect of in silico inhibition of GlmU reactions under various conditions.

Metabolite concentrations used for simulation: Low ( = 0.1xK_M), Medium ( = K_M), High ( = 10xK_M) and Intracellular levels; Inhibition strength (I/K_i ratio) maintained at 20; Numbers in the figure indicate percent decrement in GlmU overall rate due to various types of inhibition; Linear color-coded scale from Gray to White indicating decreasing level of effect of inhibition on GlmU rate).

Figure 6. Dynamics of GlcNAc1P (black line) and UTP (gray line) normalised concentrations under the proposed assay condition.

The normalized concentrations of both GlcNAc1P and UTP stay above 1 for significant portion of simulation time period, which is a favourable condition of assay for identifying uncompetitive (against E-UTP-GlcNAc1P complex) inhibitors against GlmU rxn-2.