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

Enzyme turnover numbers (k(cat)s) are essential for a quantitative understanding of cells. Because k(cat)s 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 k(cat)s using metabolic specialist Escherichia coli 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 k(cat)s 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 k(cat)s predict unseen proteomics data with much higher precision than in vitro k(cat)s. The results demonstrate that in vivo k(cat)s can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.