Letter | Published:

Automated design of synthetic ribosome binding sites to control protein expression

Nature Biotechnology volume 27, pages 946950 (2009) | Download Citation

Abstract

Microbial engineering often requires fine control over protein expression—for example, to connect genetic circuits1,2,3,4,5,6,7 or control flux through a metabolic pathway8,9,10,11,12,13. To circumvent the need for trial and error optimization, we developed a predictive method for designing synthetic ribosome binding sites, enabling a rational control over the protein expression level. Experimental validation of >100 predictions in Escherichia coli showed that the method is accurate to within a factor of 2.3 over a range of 100,000-fold. The design method also correctly predicted that reusing identical ribosome binding site sequences in different genetic contexts can result in different protein expression levels. We demonstrate the method's utility by rationally optimizing protein expression to connect a genetic sensor to a synthetic circuit. The proposed forward engineering approach should accelerate the construction and systematic optimization of large genetic systems.

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References

  1. 1.

    , , , & A synthetic multicellular system for programmed pattern formation. Nature 434, 1130–1134 (2005).

  2. 2.

    et al. A fast, robust and tunable synthetic gene oscillator. Nature 456, 516–519 (2008).

  3. 3.

    et al. Synthetic gene networks that count. Science 324, 1199–1202 (2009).

  4. 4.

    , & Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat. Biotechnol. 27, 465–471 (2009).

  5. 5.

    , & Directed evolution of a genetic circuit. Proc. Natl. Acad. Sci. USA 99, 16587–16591 (2002).

  6. 6.

    et al. A synthetic genetic edge detection program. Cell 137, 1272–1281 (2009).

  7. 7.

    , & Environmental signal integration by a modular AND gate. Mol. Syst. Biol. 3, 133 (2007).

  8. 8.

    et al. Synthetic protein scaffolds provide modular control over metabolic flux. Nat. Biotechnol. 27, 753–759 (2009).

  9. 9.

    et al. Optimization of the mevalonate-based isoprenoid biosynthetic pathway in Escherichia coli for production of the anti-malarial drug precursor amorpha-4,11-diene. Metab. Eng. 11, 13–19 (2008).

  10. 10.

    , & Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature 451, 86–89 (2008).

  11. 11.

    & Production of benzylisoquinoline alkaloids in Saccharomyces cerevisiae. Nat. Chem. Biol. 4, 564–573 (2008).

  12. 12.

    , , , & Systems metabolic engineering of Escherichia coli for L-threonine production. Mol. Syst. Biol. 3, 149 (2007).

  13. 13.

    & Combinatorial pathway analysis for improved L-tyrosine production in Escherichia coli: identification of enzymatic bottlenecks by systematic gene overexpression. Metab. Eng. 10, 69–77 (2008).

  14. 14.

    , , & Gene synthesis demystified. Trends Biotechnol. 27, 63–72 (2009).

  15. 15.

    et al. Complete chemical synthesis, assembly, and cloning of a Mycoplasma genitalium genome. Science 319, 1215–1220 (2008).

  16. 16.

    et al. Engineered riboregulators enable post-transcriptional control of gene expression. Nat. Biotechnol. 22, 841–847 (2004).

  17. 17.

    & Library of synthetic 5′ secondary structures to manipulate mRNA stability in Escherichia coli. Biotechnol. Prog. 15, 58–64 (1999).

  18. 18.

    , , & Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nat. Biotechnol. 24, 1027–1032 (2006).

  19. 19.

    & Computational design of orthogonal ribosomes. Nucleic Acids Res. 36, 4038–4046 (2008).

  20. 20.

    & Secondary structure of the ribosome binding site determines translational efficiency: a quantitative analysis. Proc. Natl. Acad. Sci. USA 87, 7668–7672 (1990).

  21. 21.

    & The influence of ribosome-binding-site elements on translational efficiency in Bacillus subtilis and Escherichia coli in vivo. Mol. Microbiol. 6, 1105–1114 (1992).

  22. 22.

    et al. Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry 37, 14719–14735 (1998).

  23. 23.

    , , & Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 288, 911–940 (1999).

  24. 24.

    , & Thermodynamics of single mismatches in RNA duplexes. Biochemistry 38, 14214–14223 (1999).

  25. 25.

    , , , & Thermodynamic analysis of 5′ and 3′ single- and 3′ double-nucleotide overhangs neighboring wobble terminal base pairs. Nucleic Acids Res. 36, 5652–5659 (2008).

  26. 26.

    & Thermodynamic characterization of the complete set of sequence symmetric tandem mismatches in RNA and an improved model for predicting the free energy contribution of sequence asymmetric tandem mismatches. Biochemistry 47, 4329–4336 (2008).

  27. 27.

    , , & Initiation of protein synthesis in bacteria. Microbiol. Mol. Biol. Rev. 69, 101–123 (2005).

  28. 28.

    & Unfolding of mRNA secondary structure by the bacterial translation initiation complex. Mol. Cell 22, 105–115 (2006).

  29. 29.

    , , & Determination of the optimal aligned spacing between the Shine-Dalgarno sequence and the translation initiation codon of Escherichia coli mRNAs. Nucleic Acids Res. 22, 4953–4957 (1994).

  30. 30.

    , , & Coding-sequence determinants of gene expression in Escherichia coli. Science 324, 255–258 (2009).

  31. 31.

    & Translational standby sites: how ribosomes may deal with the rapid folding kinetics of mRNA. J. Mol. Biol. 331, 737–743 (2003).

  32. 32.

    , , , & Thermodynamic Analysis of Interacting Nucleic Acid Strands. SIAM Rev. 49, 65–88 (2007).

  33. 33.

    , & Visualization of protein S1 within the 30S ribosomal subunit and its interaction with messenger RNA. Proc. Natl. Acad. Sci. USA 98, 11991–11996 (2001).

  34. 34.

    , & A growth model for RNA secondary structures. J. Stat. Mech. Theor. Exp. P04008 (2008).

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Acknowledgements

We are grateful to all members of the Voigt lab for technical advice and continued support. This work is supported by the Pew and Packard Foundations, Office of Naval Research, National Institutes of Health (NIH) EY016546, NIH AI067699, NSF BES-0547637, National Science Foundation (NSF) TeraGrid TG-MCB080126T and a Sandler Family Opportunity Award. C.A.V., H.M.S., and E.A.M. are part of the NSF SynBERC Engineering Research Center (http://www.synberc.org/). E.A.M. is supported by an NSF Graduate Research Fellowship and an American Society for Engineering Education National Defense Science and Engineering Graduate Fellowship.

Author information

Affiliations

  1. Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA.

    • Howard M Salis
    •  & Christopher A Voigt
  2. Graduate Group in Biophysics, University of California San Francisco, San Francisco, California, USA.

    • Ethan A Mirsky

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Contributions

H.M.S and C.A.V designed the study and wrote the manuscript. H.M.S. developed the method. H.M.S. and E.A.M. performed the experiments.

Corresponding author

Correspondence to Christopher A Voigt.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figs. 1–11, Supplementary Discussion and Supplementary Methods

Excel files

  1. 1.

    Supplementary Table I

    A table of all ribosome binding site sequences created in this study, their predicted Gtot, their measured protein expression levels, and doubling times.

Text files

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    Supplementary Data

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DOI

https://doi.org/10.1038/nbt.1568

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