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Lecture #14

Lecture #14. Regulatory Enzymes. Outline. Phosphofructokinase-1 Describing the bound states of activators and inhibitors Integration with glycolysis. Phosphofructokinase-1. Metabolic Role. Background. Tetramer 3 Isoforms: M,L,P (muscle, liver, platelet)

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Lecture #14

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  1. Lecture #14 Regulatory Enzymes

  2. Outline • Phosphofructokinase-1 • Describing the bound states of activators and inhibitors • Integration with glycolysis

  3. Phosphofructokinase-1

  4. Metabolic Role

  5. Background • Tetramer • 3 Isoforms: M,L,P (muscle, liver, platelet) • 2 Natural Forms: R,T (relaxed, tight) • Known inhibitors: ATP, citrate, PEP • Known activators: AMP, cAMP, Pi, SO4, FBP • Catalytic Activity:

  6. PFK sub-network

  7. The Catalytic Mechanism:binding of the two substrates followed by the chemical reaction 1) 2) 3)

  8. AMP and ATP as regulatory ligands activation conformation inhibition

  9. Stoichiometric Matrix

  10. Pools and Ratios • PFK – R state • All forms of R0 + R1 + R2 + R3 + R4 • PFK – T state • All forms of T0 + T1 + T2 + T3 + T4 • PFK – R catalytic state • All forms of Ri,AF • Ratios • At steady state ~ rR = 90%, rcat = 12%

  11. DETERMINING THE STEADY STATE

  12. Let’s revisit the subnetwork Equilibrium v = 0 Steady State

  13. Constraints on the Network • Total mass balance: • Total flux: • Known equilibrium constants

  14. Solving for the concentrations Note: When equilibrium constants are plugged in, all forward rate constants in equilibrium reactions fall out, leaving only the catalytic rate constants

  15. Estimating the catalytic rate constants Chosen Steady State kPFK kATP kF6P

  16. INTEGRATION WITH GLYCOLYSIS

  17. Stoichiometric Matrix

  18. DYNAMIC SIMULATIONS

  19. Dynamic Simulation • Two perturbations • Standard 50% increase in ATP utilization • Additional 15% decrease in ATP utilization

  20. Glycolysis Dynamics

  21. Glycolysis Dynamics 50% increase in ATP utilization 15% decrease in ATP utilization

  22. Summary • Enzymes can be explicitly represented in simulation modules as molecules • Enzymes have many binding states • Binding of regulators (inhibitors and activators) alters protein activity; leading to a ‘tug of war’ amongst the functional states (i.e. T and R) • Ratios that represent what fraction of the enzyme is in an active or inhibited functional states can be formed • Enzyme sub-networks can be seamlessly integrated with the scaffold metabolic network • Regulator binding to PFK, a key glycolytic regulatory enzyme, was demonstrated

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