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

酶的周转数（k _cat）对于定量了解细胞至关重要。因为传统上是在低通量分析中测量k _cat的，所以它们可能不一致，需要大量劳动，并且可能错过体内效应。我们使用数据驱动的方法在体内估计ķ_猫S使用新陈代谢专家大肠杆菌是导致从中央代谢基因敲除，然后通过实验室进化代谢优化株。通过将绝对蛋白质组学与通量组学数据相结合，我们发现体内k _catS对基因扰动具有鲁棒性，表明对基因丢失的代谢适应主要是通过其他机制来实现的，例如基因调控改变。结合机器学习和基因组规模的代谢模型，我们表明，所获得的体内k _cat预测看不见的蛋白质组学数据的准确性要比体外k _cat高得多。结果表明，体内k _cat可以解决基因组规模细胞模型参数不一致和覆盖率低的问题。