The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Enzyme clustering accelerates processing of intermediates through metabolic channeling
Abstract
We present a quantitative model to demonstrate that coclustering multiple enzymes into compact agglomerates accelerates the processing of intermediates, yielding the same efficiency benefits as direct channeling, a well-known mechanism in which enzymes are funneled between enzyme active sites through a physical tunnel. The model predicts the separation and size of coclusters that maximize metabolic efficiency, and this prediction is in agreement with previously reported spacings between coclusters in mammalian cells. For direct validation, we study a metabolic branch point in Escherichia coli and experimentally confirm the model prediction that enzyme agglomerates can accelerate the processing of a shared intermediate by one branch, and thus regulate steady-state flux division. Our studies establish a quantitative framework to understand coclustering-mediated metabolic channeling and its application to both efficiency improvement and metabolic regulation.
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Cites
Construction of Escherichia coli K‐12 in‐frame, single‐gene knockout mutants: the Keio collection
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Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells
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The functional interactome landscape of the human histone deacetylase family
Joshi, P., Greco, T. M., Guise, A. J., Luo, Y., Yu, F., Nesvizhskii, A. I., & Cristea, I. M. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.26 · Cited by 289
Self-citationOn the role of organized multienzyme systems in cellular metabolism: a general synthesis
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“(4) This implies a maximal local enzyme density nmax that cannot be exceeded27, which we modeled by the constraint”
Proteomic Characterization of Pseudorabies Virus Extracellular Virions
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Self-citationMapping protein-protein proximity in the purinosome
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Advances and prospects in metabolic engineering of Zymomonas mobilis
Wang, X., He, Q., Yang, Y., Wang, J., Haning, K., Hu, Y., Wu, B., He, M., Zhang, Y., Bao, J., et al. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.04.001 · Cited by 154
Jeckelmann, J.-M., & Erni, B. (2019) · Subcellular Biochemistry · doi:10.1007/978-3-030-18768-2_8 · Cited by 55
An Experimental Framework for Generating Evolvable Chemical Systems in the Laboratory
Baum, D. A., & Vetsigian, K. (2017) · Origins of Life and Evolution of Biospheres · doi:10.1007/s11084-016-9526-x · Cited by 40
Jasinska, W., Dindo, M., Cordoba, S. M. C., Serohijos, A. W. R., Laurino, P., Brotman, Y., & Bershtein, S. (2024) · Nature Communications · doi:10.1038/s41467-024-49646-7 · Cited by 21
Enzyme co-localisation: Mechanisms and benefits
Kondrat, S., Krauss, U., & von Lieres, E. (2022) · Current Research in Chemical Biology · doi:10.1016/j.crchbi.2022.100031 · Cited by 19
Metadata
- Authors
- Castellana, M., Wilson, M. Z., Xu, Y., Joshi, P., Cristea, I. M., Rabinowitz, J. D., Gitai, Z., & Wingreen, N. S.
- Venue
- Nature Biotechnology
- Published
- 2014-09-28
- Volume
- 32
- Issue
- 10
- Pages
- 1011-1018
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W1964478423
- Cited by
- 445
- References
- 8
- FWCI
- 10.59
- Type
- journal article
- Language
- en
- Open access
- closed
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