TY  - JOUR
A1  - Xu, Rudan
A1  - Razaghi-Moghadam, Zahra
A1  - Nikoloski, Zoran
T1  - Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli
T2  - Bioinformatics
N2  - Motivation: 
Constraint-based modeling approaches allow the estimation of maximal in vivo enzyme 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.

Results:
Here, 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 from Escherichia 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 maximal in vivo catalytic rates estimated by NIDLE-flux exhibits a Pearson correlation of 0.74 with in vitro enzyme turnover numbers. However, NIDLE-flux results in a 1.4-fold increase in the size of the estimated maximal in vivo catalytic rates in comparison to the contenders. Integration of the maximum in vivo catalytic 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.
Y1  - 2021
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/59946
SN  - 1367-4803
SN  - 1460-2059
VL  - 37
IS  - 21
SP  - 3848
EP  - 3855
PB  - Oxford Univ. Press
CY  - Oxford
ER  -