Data integration across conditions improves turnover number estimates and metabolic predictions

Philipp Wendering, Marius Arend, Zahra Razaghi-Moghadam and Zoran Nikoloski ()
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Philipp Wendering: University of Potsdam
Marius Arend: University of Potsdam
Zahra Razaghi-Moghadam: Max Planck Institute of Molecular Plant Physiology
Zoran Nikoloski: University of Potsdam

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-37151-2

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