Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers.

Abstract

Enzyme turnover numbers (<i>k</i>_cats) are essential for a quantitative understanding of cells. Because <i>k</i>_cats are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo <i>k</i>_cats using metabolic specialist <i>Escherichia coli</i> strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo <i>k</i>_cats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo <i>k</i>_cats predict unseen proteomics data with much higher precision than in vitro <i>k</i>_cats. The results demonstrate that in vivo <i>k</i>_cats can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.