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Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations

Author

Listed:
  • Asli Sahin

    (Ecole Polytechnique Federale de Lausanne (EPFL))

  • Daniel R. Weilandt

    (Ecole Polytechnique Federale de Lausanne (EPFL)
    Princeton University)

  • Vassily Hatzimanikatis

    (Ecole Polytechnique Federale de Lausanne (EPFL))

Abstract

Deciphering the metabolic functions of organisms requires understanding the dynamic responses of living cells upon genetic and environmental perturbations, which in turn can be inferred from enzymatic activity. In this work, we investigate the optimal modes of operation for enzymes in terms of the evolutionary pressure driving them toward increased catalytic efficiency. We develop a framework using a mixed-integer formulation to assess the distribution of thermodynamic forces and enzyme states, providing detailed insights into the enzymatic mode of operation. We use this framework to explore Michaelis-Menten and random-ordered multi-substrate mechanisms. We show that optimal enzyme utilization is achieved by unique or alternative operating modes dependent on reactant concentrations. We find that in a bimolecular enzyme reaction, the random mechanism is optimal over any other ordered mechanism under physiological conditions. Our framework can investigate the optimal catalytic properties of complex enzyme mechanisms. It can further guide the directed evolution of enzymes and fill in the knowledge gaps in enzyme kinetics.

Suggested Citation

  • Asli Sahin & Daniel R. Weilandt & Vassily Hatzimanikatis, 2023. "Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38159-4
    DOI: 10.1038/s41467-023-38159-4
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    References listed on IDEAS

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    1. David Heckmann & Daniel C. Zielinski & Bernhard O. Palsson, 2018. "Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
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    4. Pierre Salvy & Vassily Hatzimanikatis, 2020. "The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models," Nature Communications, Nature, vol. 11(1), pages 1-17, December.
    5. David E. Kaufman & Robert L. Smith, 1998. "Direction Choice for Accelerated Convergence in Hit-and-Run Sampling," Operations Research, INFORMS, vol. 46(1), pages 84-95, February.
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