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Optimizing the Substrate Uptake Rate of Solute Carriers
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The diversity in solute carriers arose from evolutionary pressure. Here, we surmised that the adaptive search for optimizing the rate of substrate translocation was also shaped by the ambient extracellular and intracellular concentrations of substrate and co-substrate(s). We explored possible solutions by employing kinetic models, which were based on analytical expressions of the substrate uptake rate, that is, as a function of the microscopic rate constants used to parameterize the transport cycle. We obtained the defining terms for five reaction schemes with identical transport stoichiometry (i.e., Na+: substrate = 2:1). We then utilized an optimization algorithm to find the set of numeric values for the microscopic rate constants, which provided the largest value for the substrate uptake rate: The same optimized rate was achieved by different sets of numerical values for the microscopic rate constants. An in-depth analysis of these sets provided the following insights: (i) In the presence of a low extracellular substrate concentration, a transporter can only cycle at a high rate, if it has low values for both, the Michaelis–Menten constant (KM) for substrate and the maximal substrate uptake rate (Vmax). (ii) The opposite is true for a transporter operating at high extracellular substrate concentrations. (iii) Random order of substrate and co-substrate binding is superior to sequential order, if a transporter is to maintain a high rate of substrate uptake in the presence of accumulating intracellular substrate. Our kinetic models provide a framework to understand how and why the transport cycles of closely related transporters differ.
Title: Optimizing the Substrate Uptake Rate of Solute Carriers
Description:
The diversity in solute carriers arose from evolutionary pressure.
Here, we surmised that the adaptive search for optimizing the rate of substrate translocation was also shaped by the ambient extracellular and intracellular concentrations of substrate and co-substrate(s).
We explored possible solutions by employing kinetic models, which were based on analytical expressions of the substrate uptake rate, that is, as a function of the microscopic rate constants used to parameterize the transport cycle.
We obtained the defining terms for five reaction schemes with identical transport stoichiometry (i.
e.
, Na+: substrate = 2:1).
We then utilized an optimization algorithm to find the set of numeric values for the microscopic rate constants, which provided the largest value for the substrate uptake rate: The same optimized rate was achieved by different sets of numerical values for the microscopic rate constants.
An in-depth analysis of these sets provided the following insights: (i) In the presence of a low extracellular substrate concentration, a transporter can only cycle at a high rate, if it has low values for both, the Michaelis–Menten constant (KM) for substrate and the maximal substrate uptake rate (Vmax).
(ii) The opposite is true for a transporter operating at high extracellular substrate concentrations.
(iii) Random order of substrate and co-substrate binding is superior to sequential order, if a transporter is to maintain a high rate of substrate uptake in the presence of accumulating intracellular substrate.
Our kinetic models provide a framework to understand how and why the transport cycles of closely related transporters differ.
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