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The protein translation machinery is expressed for maximal efficiency in Escherichia coli

Author

Listed:
  • Xiao-Pan Hu

    (Heinrich Heine University)

  • Hugo Dourado

    (Heinrich Heine University)

  • Peter Schubert

    (Heinrich Heine University)

  • Martin J. Lercher

    (Heinrich Heine University)

Abstract

Protein synthesis is the most expensive process in fast-growing bacteria. Experimentally observed growth rate dependencies of the translation machinery form the basis of powerful phenomenological growth laws; however, a quantitative theory on the basis of biochemical and biophysical constraints is lacking. Here, we show that the growth rate-dependence of the concentrations of ribosomes, tRNAs, mRNA, and elongation factors observed in Escherichia coli can be predicted accurately from a minimization of cellular costs in a mechanistic model of protein translation. The model is constrained only by the physicochemical properties of the molecules and has no adjustable parameters. The costs of individual components (made of protein and RNA parts) can be approximated through molecular masses, which correlate strongly with alternative cost measures such as the molecules’ carbon content or the requirement of energy or enzymes for their biosynthesis. Analogous cost minimization approaches may facilitate similar quantitative insights also for other cellular subsystems.

Suggested Citation

  • Xiao-Pan Hu & Hugo Dourado & Peter Schubert & Martin J. Lercher, 2020. "The protein translation machinery is expressed for maximal efficiency in Escherichia coli," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18948-x
    DOI: 10.1038/s41467-020-18948-x
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    Cited by:

    1. Yuping Chen & Jo-Hsi Huang & Connie Phong & James E. Ferrell, 2024. "Viscosity-dependent control of protein synthesis and degradation," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Manlu Zhu & Xiongfeng Dai, 2024. "Shaping of microbial phenotypes by trade-offs," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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