Data integration across conditions improves turnover number estimates and metabolic predictions

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...

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Bibliographic Details
Published in:Nature communications 2023-03, Vol.14 (1), p.1485-12, Article 1485
Main Authors: Wendering, Philipp, Arend, Marius, Razaghi-Moghadam, Zahra, Nikoloski, Zoran
Format: Article
Language:English
Subjects:
631/114/2390
631/114/2397
631/1647/48
631/61/318
Constraint modelling
Data integration
E coli
Enzymes
Escherichia coli - metabolism
Estimates
Genomes
Growth rate
Humanities and Social Sciences
Metabolic Networks and Pathways
Metabolism
Models, Biological
multidisciplinary
Phenotypes
Physiology
Predictions
Proteins
Proteomics
Science
Science (multidisciplinary)
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Summary: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. The construction of protein-constrained genome-scale metabolic models depends on the integration of organism-specific enzyme turnover numbers. Here, the authors show that correction of turnover numbers by simultaneous consideration of proteomics and physiological data leads to improved predictions of condition-specific growth rates.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-37151-2