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

Abstract

Turnover numbers (kcat values) quantitatively represent the activity of enzymes, which are mostly measured in vitro. While a few studies have reported in vivo catalytic rates (kapp values) in bacteria, a large-scale estimation of kapp in eukaryotes is lacking. Here, we estimated kapp of the yeast Saccharomyces cerevisiae under diverse conditions. By comparing the maximum kapp across conditions with in vitro kcat 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 kcat 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 kapp dataset we observed better performance than the default model with in vitro kcat in predicting proteomics data, demonstrating the strength of using the dataset generated here.

Yeast has evolved to minimize protein resource cost for synthesizing amino acids

“…To estimate the substrate (i.e., glucose) and protein costs of synthesizing amino acids in S. cerevisiae , we used the GEM Yeast8 ( 10 ) integrated with the in vivo enzyme catalytic rate ( k app ) dataset ( 11 ) ( SI Appendix ). Note that we adopted the maximum k app ( k max ) across conditions to calculate the generic protein costs of synthesizing amino acids, referred to as k max -based protein costs, which could represent the potential.…”

“…By removing tryptophan, we still found that the correlation between the logarithms of the average relative abundances of amino acids and protein costs of synthesizing amino acids (Pearson’s r = −0.89) is better than glucose costs (Pearson’s r = −0.28), suggesting that our hypothesis is valid. Moreover, given that yeast in vitro k cat might also differ from in vivo k max ( 11 ), we performed random sampling for all the yeast in vitro k cat and nonyeast k cat simultaneously to reestimate protein costs of synthesizing 20 amino acids and then correlated them with the logarithms of the average relative abundances of amino acids ( SI Appendix ). By sampling 1,000 times we obtained 1,000 Pearson’s r values and found that less than 5% of them were greater than the Pearson’s r value of using glucose costs (−0.5).…”

“…To implement protein cost information, the enzymatic reactions in Yeast8 ( 10 ) were integrated with either in vivo k max or condition-specific k app ( 11 ) based on the enzyme-constrained modeling frameworks ( 17 , 18 ). For the reactions without available in vivo data, in vitro k cat or nonyeast data were assigned instead ( 15 ).…”

High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering

“…The in vitro k cat values of yeast were further analyzed by Chen et al and the authors found that the weak correlation is caused by the heterologous expression of enzymes. Exclusion of these heterologous expressed enzymes led to a better R 2 = 0.41 but still not as good as for E. coli [8] .…”

“… [9] and Chen et al . [8] then applied k app to the enzyme-constrained metabolic model and found that model with k app showed better performance in compared with the original one. It is clear that flux data and proteome data from the well characterized species under the certain cultivation conditions are available and reliable.…”