AbstractMotivationConstraint-based modeling approaches allow the estimation of maximalin vivoenzyme catalytic rates that can serve as proxies for enzyme turnover numbers. Yet, genome-scale flux profiling remains a challenge in deploying these approaches to catalogue proxies for enzyme catalytic rates across organisms.ResultsHere, we formulate a constraint-based approach, termed NIDLE-flux, to estimate fluxes at a genome-scale level by using the principle of efficient usage of expressed enzymes. Using proteomics data fromEscherichia coli, we show that the fluxes estimated by NIDLE-flux and the existing approaches are in excellent qualitative agreement (Pearson correlation > 0.9). We also find that the maximalin vivocatalytic rates estimated by NIDLE-flux exhibits a Pearson correlation of 0.74 within vitroenzyme turnover numbers. However, NIDLE-flux results in a 1.4-fold increase in the size of the estimated maximalin vivocatalytic rates in comparison to the contenders. Integration of the maximumin vivocatalytic rates with publically available proteomics and metabolomics data provide a better match to fluxes estimated by NIDLE-flux. Therefore, NIDLE-flux facilitates more effective usage of proteomics data to estimate proxies for kcatomes.Availability and implementationhttps://github.com/Rudan-X/NIDLE-flux-code.Supplementary informationSupplementary dataare available atBioinformaticsonline.