Abstract
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 R(2) = 0.28, which is lower than for Escherichia coli (R(2) = 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 R(2) = 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.