Turnover numbers (k_cat values) quantitatively represent the activity of enzymes, which are mostly measured in vitro. While a few studies have reported in vivo catalytic rates (k_app values) in bacteria, a large-scale estimation of k_app in eukaryotes is lacking. Here, we estimated k_app of the yeast Saccharomyces cerevisiae under diverse conditions. By comparing the maximum k_app across conditions with in vitro k_cat we found a weak correlation in log scale of R² = 0.28, which is lower than for Escherichia coli (R² = 0.62). The weak correlation is caused by the fact that many in vitro k_cat values were measured for enzymes obtained through heterologous expression. Removal of these enzymes improved the correlation to R² = 0.41 but still not as good as for E. coli, suggesting considerable deviations between in vitro and in vivo enzyme activities in yeast. By parameterizing an enzyme-constrained metabolic model with our k_app dataset we observed better performance than the default model with in vitro k_cat in predicting proteomics data, demonstrating the strength of using the dataset generated here.

周转数（k _cat值）定量地代表酶的活性，其主要在体外测量。虽然一些研究报告了细菌的体内催化速率（k _app值），但缺乏对真核生物中k _app的大规模估计。在这里，我们估计了酵母酿酒酵母在不同条件下的k _app。通过将不同条件下的最大k _app与体外k _cat进行比较，我们发现R ² = 0.28 的对数尺度的相关性较弱，这低于大肠杆菌（R ² = 0.62)。弱相关性是由于对通过异源表达获得的酶测量了许多体外k _cat值这一事实造成的。去除这些酶改善了与R ² = 0.41的相关性，但仍不如大肠杆菌，这表明酵母中体外和体内酶活性之间存在相当大的偏差。通过使用我们的k _app数据集参数化酶约束代谢模型，我们观察到比使用体外k _cat的默认模型在预测蛋白质组学数据方面具有更好的性能，证明了使用此处生成的数据集的优势。