English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli

Xu, R., Razaghi-Moghadam, Z., & Nikoloski, Z. (2021). Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli. Bioinformatics. doi:10.1093/bioinformatics/btab575.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files

Locators

show
hide
Locator:
Link (Any fulltext)
Description:
-

Creators

show
hide
 Creators:
Xu, Rudan1, Author
Razaghi-Moghadam, Z.2, Author              
Nikoloski, Z.2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              

Content

show
hide
Free keywords: -
 Abstract: 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.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.https://github.com/Rudan-X/NIDLE-flux-code.Supplementary data are available at Bioinformatics online.

Details

show
hide
Language(s): eng - English
 Dates: 2021-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btab575
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Bioinformatics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991