Data integration across conditions improves turnover number estimates and metabolic predictions
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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.
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- Authors
- Wendering, P., Arend, M., Razaghi-Moghadam, Z., & Nikoloski, Z.
- Affiliations
- University of Potsdam (DE)
- Corresponding author
- Nikoloski, Zoran
- Venue
- Nature Communications
- Published
- 2023-03-17
- Volume
- 14
- Issue
- 1
- Pages
- 1485-1485
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10023748
- OpenAlex
- W4327742365
- Cited by
- 23
- References
- 49
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 8 · 2023: 7
- FWCI
- 2.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- EC | Horizon 2020 Framework Programme (862201, 739582)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- enzyme turnover number, constraint-based metabolic modeling, Escherichia coli, Saccharomyces cerevisiae, enzyme abundance, metabolic phenotyping
- MeSH terms
- Escherichia coli, Models, Biological, Metabolic Networks and Pathways
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