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Data integration across conditions improves turnover number estimates and metabolic predictions

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  • Philipp Wendering

    (University of Potsdam
    Max Planck Institute of Molecular Plant Physiology)

  • Marius Arend

    (University of Potsdam
    Max Planck Institute of Molecular Plant Physiology)

  • Zahra Razaghi-Moghadam

    (Max Planck Institute of Molecular Plant Physiology)

  • Zoran Nikoloski

    (University of Potsdam
    Max Planck Institute of Molecular Plant Physiology)

Abstract

Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase the prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by integrating reaction rate and enzyme abundance measurements from individual experiments. Yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here, we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly when protein abundances are considered. We demonstrate that correction of turnover numbers by simultaneous consideration of proteomics and physiological data leads to improved predictions of condition-specific growth rates. Moreover, the obtained estimates are more precise than corresponding in vitro turnover numbers. Therefore, our approach provides the means to correct turnover numbers and paves the way towards cataloguing kcatomes of other organisms.

Suggested Citation

  • Philipp Wendering & Marius Arend & Zahra Razaghi-Moghadam & Zoran Nikoloski, 2023. "Data integration across conditions improves turnover number estimates and metabolic predictions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37151-2
    DOI: 10.1038/s41467-023-37151-2
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    References listed on IDEAS

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    1. Marius Arend & David Zimmer & Rudan Xu & Frederik Sommer & Timo Mühlhaus & Zoran Nikoloski, 2023. "Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

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