In vitro turnover numbers do not reflect in vivo activities of yeast enzymes

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 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.

Keywords: Saccharomyces cerevisiae; kcat; metabolism; proteomics; turnover number.

Fig. 1.

Analysis of k_app and k_max of S. cerevisiae. Correlation in log scale between k_max and in vitro k_cat for all data points (A) and for the data points in which in vitro k_cat were measured using enzymes obtained through heterologous expression (B) and through homologous expression (C). The data points with deviations more than two orders of magnitude are labeled by the enzyme names. Student’s t test was used to calculate P value for Pearson’s correlation. (D) Change in average k_app/k_max of each condition with growth rate. (E) Comparison between k_app/k_max of individual reactions in two groups divided by a growth rate of 0.2/h. A two-sided Wilcoxon rank sum test was used to calculate P value.

Fig. 2.

Predictions of proteomics data on various carbon sources by ecYeast8 parameterized with an assumed same k_cat, default in vitro k_cat, general k_max, and μ-dependent k_max. Model performance is evaluated by root-mean-square error (RMSE) between predicted and measured protein levels on a log10 scale. N is the number of proteins with predicted nonzero concentrations by four parameterization strategies.