Work

Data integration across conditions improves turnover number estimates and metabolic predictions

Year: 2023

Type: article

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.

Source: Nature Communications

DOI: 10.1038/s41467-023-37151-2

Authors: Philipp Wendering, Marius Arend, Zahra Razaghi-Moghadam, Zoran Nikoloski

Institutions: University of Potsdam, Max Planck Institute of Molecular Plant Physiology

Language: English

Topic: Microbial Metabolic Engineering and Bioproduction

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $6,290

Funders: European Commission, Max-Planck-Gesellschaft, Universität Potsdam, Horizon 2020 Framework Programme