Abstract

Genetic regulatory proteins inducible by small molecules are useful synthetic biology tools as sensors and switches. Bacterial allosteric transcription factors (aTFs) are a major class of regulatory proteins, but few aTFs have been redesigned to respond to new effectors beyond natural aTF-inducer pairs. Altering inducer specificity in these proteins is difficult because substitutions that affect inducer binding may also disrupt allostery. We engineered an aTF, the Escherichia coli lac repressor, LacI, to respond to one of four new inducer molecules: fucose, gentiobiose, lactitol and sucralose. Using computational protein design, single-residue saturation mutagenesis or random mutagenesis, along with multiplex assembly, we identified new variants comparable in specificity and induction to wild-type LacI with its inducer, isopropyl β-D-1-thiogalactopyranoside (IPTG). The ability to create designer aTFs will enable applications including dynamic control of cell metabolism, cell biology and synthetic gene circuits.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Gene Expression Omnibus

Referenced accessions

References

  1. 1.

    & A family of bacterial regulators homologous to Gal and Lac repressors. J. Biol. Chem. 267, 15869–15874 (1992).

  2. 2.

    Molecular biology of the LysR family of transcriptional regulators. Annu. Rev. Microbiol. 47, 597–626 (1993).

  3. 3.

    , , , & Arac/XylS family of transcriptional regulators. Microbiol. Mol. Biol. Rev. 61, 393–410 (1997).

  4. 4.

    et al. The TetR family of transcriptional repressors. Microbiol. Mol. Biol. Rev. 69, 326–356 (2005).

  5. 5.

    & Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Res. 25, 1203–1210 (1997).

  6. 6.

    , , & Transcription factor-based screens and synthetic selections for microbial small-molecule biosynthesis. ACS Synth. Biol. 2, 47–58 (2013).

  7. 7.

    , , & Evolution-guided optimization of biosynthetic pathways. Proc. Natl. Acad. Sci. USA 111, 17803–17808 (2014).

  8. 8.

    , & Next-generation synthetic gene networks. Nat. Biotechnol. 27, 1139–1150 (2009).

  9. 9.

    , & High-throughput metabolic engineering: advances in small-molecule screening and selection. Annu. Rev. Biochem. 79, 563–590 (2010).

  10. 10.

    & Design and application of a mevalonate-responsive regulatory protein. Angew. Chem. Int. Edn Engl. 50, 1084–1086 (2011).

  11. 11.

    , , & Evolutionarily conserved networks of residues mediate allosteric communication in proteins. Nat. Struct. Biol. 10, 59–69 (2003).

  12. 12.

    , , , & Genetic studies of the lac repressor. XIV. Analysis of 4000 altered Escherichia coli lac repressors reveals essential and non-essential residues, as well as “spacers” which do not require a specific sequence. J. Mol. Biol. 240, 421–433 (1994).

  13. 13.

    , , , & Engineering allostery. Trends Genet. 30, 521–528 (2014).

  14. 14.

    , & Directed evolution of Vibrio fischeri LuxR for increased sensitivity to a broad spectrum of acyl-homoserine lactones. Mol. Microbiol. 55, 712–723 (2005).

  15. 15.

    , & Effector specificity mutants of the transcriptional activator NahR of naphthalene degrading Pseudomonas define protein sites involved in binding of aromatic inducers. J. Biol. Chem. 272, 3986–3992 (1997).

  16. 16.

    & Generation of novel bacterial regulatory proteins that detect priority pollutant phenols. Appl. Environ. Microbiol. 66, 163–169 (2000).

  17. 17.

    , & Emergence of novel functions in transcriptional regulators by regression to stem protein types. Mol. Microbiol. 65, 907–919 (2007).

  18. 18.

    , , , & Teaching TetR to recognize a new inducer. J. Mol. Biol. 329, 217–227 (2003).

  19. 19.

    , & AraC regulatory protein mutants with altered effector specificity. J. Am. Chem. Soc. 130, 5267–5271 (2008).

  20. 20.

    , , , & Rosetta comparative modeling for library design: engineering alternative inducer specificity in a transcription factor. Proteins 10.1002/prot.24828 (13 May 2015).

  21. 21.

    , , & Engineering transcriptional regulator effector specificity using computational design and in vitro rapid prototyping: developing a vanillin sensor. ACS Synth. Biol. 10.1021/acssynbio.5b00090 (19 August 2015).

  22. 22.

    et al. Genome scale reconstruction of a Salmonella metabolic model: comparison of similarity and differences with a commensal Escherichia coli strain. J. Biol. Chem. 284, 29480–29488 (2009).

  23. 23.

    et al. De novo computational design of retro-aldol enzymes. Science 319, 1387–1391 (2008).

  24. 24.

    et al. Kemp elimination catalysts by computational enzyme design. Nature 453, 190–195 (2008).

  25. 25.

    et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–216 (2013).

  26. 26.

    et al. Scalable gene synthesis by selective amplification of DNA pools from high-fidelity microchips. Nat. Biotechnol. 28, 1295–1299 (2010).

  27. 27.

    , , , & Plasticity of quaternary structure: twenty-two ways to form a LacI dimer. Protein Sci. 10, 262–276 (2001).

  28. 28.

    , , , & Perturbation from a distance: mutations that alter LacI function through long-range effects. Biochemistry 42, 14004–14016 (2003).

  29. 29.

    & Flexibility in the inducer binding region is crucial for allostery in the Escherichia coli lactose repressor. Biochemistry 48, 4988–4998 (2009).

  30. 30.

    Recombineering with tolC as a selectable/counter-selectable marker: remodeling the rRNA operons of Escherichia coli. Nucleic Acids Res. 36, e4 (2008).

  31. 31.

    et al. Synthetic biosensors for precise gene control and real-time monitoring of metabolites. Nucleic Acids Res. 43, 7648–7660 (2015).

  32. 32.

    & Using orthologous and paralogous proteins to identify specificity-determining residues in bacterial transcription factors. J. Mol. Biol. 321, 7–20 (2002).

  33. 33.

    , , & Prediction of functional specificity determinants from protein sequences using log-likelihood ratios. Bioinformatics 22, 164–171 (2006).

  34. 34.

    & A closer view of the conformation of the Lac repressor bound to operator. Nat. Struct. Biol. 7, 209–214 (2000).

  35. 35.

    & Controlling gene expression in living cells through small molecule-RNA interactions. Science 282, 296–298 (1998).

  36. 36.

    , , & Directed evolution of protein switches and their application to the creation of ligand-binding proteins. Proc. Natl. Acad. Sci. USA 102, 11224–11229 (2005).

  37. 37.

    & A three-hybrid system for detecting small ligand-protein receptor interactions. Proc. Natl. Acad. Sci. USA 93, 12817–12821 (1996).

  38. 38.

    , , , & A directed approach for engineering conditional protein stability using biologically silent small molecules. J. Biol. Chem. 282, 24866–24872 (2007).

  39. 39.

    et al. Screening and identification of a fungal β-glucosidase and the enzymatic synthesis of gentiooligosaccharide. Appl. Biochem. Biotechnol. 163, 1012–1019 (2011).

  40. 40.

    & Tight control of gene expression in mammalian cells by tetracycline-responsive promoters. Proc. Natl. Acad. Sci. USA 89, 5547–5551 (1992).

  41. 41.

    et al. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898 (2009).

  42. 42.

    & One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl. Acad. Sci. USA 97, 6640–6645 (2000).

  43. 43.

    , , , & Engineering and characterization of a superfolder green fluorescent protein. Nat. Biotechnol. 24, 79–88 (2006).

  44. 44.

    et al. Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and the Cambridge Structural Database. J. Chem. Inf. Model. 50, 572–584 (2010).

  45. 45.

    & Conformer generation with OMEGA: learning from the data set and the analysis of failures. J. Chem. Inf. Model. 52, 2919–2936 (2012).

  46. 46.

    XDS. Acta Crystallogr. D Biol. Crystallogr. 66, 125–132 (2010).

  47. 47.

    et al. Toward the structural genomics of complexes: crystal structure of a PE/PPE protein complex from Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. USA 103, 8060–8065 (2006).

  48. 48.

    , , & Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).

  49. 49.

    , & Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr. D Biol. Crystallogr. 53, 240–255 (1997).

  50. 50.

    , & Use of TLS parameters to model anisotropic displacements in macromolecular refinement. Acta Crystallogr. D Biol. Crystallogr. 57, 122–133 (2001).

  51. 51.

    et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

  52. 52.

    et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).

  53. 53.

    , & Purification and properties of Gal repressor:pL-galR fusion in pKC31 plasmid vector. J. Biol. Chem. 262, 2326–2331 (1987).

  54. 54.

    et al. Novel insights from hybrid LacI/GalR proteins: family-wide functional attributes and biologically significant variation in transcription repression. Nucleic Acids Res. 40, 11139–11154 (2012).

  55. 55.

    & FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

  56. 56.

    BLAT--the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

  57. 57.

    , , & A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

  58. 58.

    ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

  59. 59.

    et al. Genetic studies of the Lac repressor. XV: 4000 single amino acid substitutions and analysis of the resulting phenotypes on the basis of the protein structure. J. Mol. Biol. 261, 509–523 (1996).

Download references

Acknowledgements

We thank B. Turczyk and D. Weigand for synthesizing the single-amino-acid substitution library on the Custom Array synthesizer, and G. Cuneo and V. Toxavidis for assistance with flow cytometry and FACS. We thank Rosetta@home participants for providing the computing resources necessary for this work. This work was supported by the US Department of Energy (DOE) (DE-FG02-02ER63445 to G.M.C.), a Wyss Technology Development Fellowship (to S.R.) and the US National Institute of General Medical Sciences (grant 1P41 GM103533 to S.F.). The sucralose-responsive LacI mutant was purified and crystallized with assistance from the UCLA-DOE Protein Expression Technology Center, the UCLA-DOE X-ray Crystallography Core Facility (both supported by DOE grant DE-FC02-02ER63421) and the UCLA Crystallization Core Facility; in particular we thank M. Collazo for help with protein crystallization. X-ray data collection was facilitated by M. Capel, K. Rajashankar, N. Sukumar, F. Murphy and I. Kourinov of the Northeastern Collaborative Access Team beamline 24-ID-C at the Advanced Photon Source of Argonne National Laboratory, which is supported by US National Institutes of Health grants P41 RR015301 and P41 GM103403. Use of the Advanced Photon Source is supported by the DOE under contract DE-AC02-06CH11357.

Author information

Author notes

    • Rocco Moretti
    •  & Srivatsan Raman

    Present addresses: Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, USA (R.M.); Department of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin, USA (S.R.).

Affiliations

  1. Wyss Institute for Biologically-Inspired Engineering, Harvard University, Boston, Massachusetts, USA.

    • Noah D Taylor
    • , Alexander S Garruss
    • , Jameson K Rogers
    • , George M Church
    •  & Srivatsan Raman
  2. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.

    • Noah D Taylor
    • , Alexander S Garruss
    • , Jameson K Rogers
    • , George M Church
    •  & Srivatsan Raman
  3. Department of Biochemistry, University of Washington, Seattle, Washington, USA.

    • Rocco Moretti
    •  & David Baker
  4. Howard Hughes Medical Institute, University of Washington, Seattle, Washington, USA.

    • Rocco Moretti
    • , David Baker
    •  & Stanley Fields
  5. University of California Los Angeles–Department of Energy Institute for Genomics and Proteomics, University of California Los Angeles, Los Angeles, California, USA.

    • Sum Chan
    • , Mark A Arbing
    •  & Duilio Cascio
  6. Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut, USA.

    • Farren J Isaacs
  7. Systems Biology Institute, Yale University, West Haven, Connecticut, USA.

    • Farren J Isaacs
  8. Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California, USA.

    • Sriram Kosuri
  9. Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

    • Stanley Fields
  10. Department of Medicine, University of Washington, Seattle, Washington, USA.

    • Stanley Fields

Authors

  1. Search for Noah D Taylor in:

  2. Search for Alexander S Garruss in:

  3. Search for Rocco Moretti in:

  4. Search for Sum Chan in:

  5. Search for Mark A Arbing in:

  6. Search for Duilio Cascio in:

  7. Search for Jameson K Rogers in:

  8. Search for Farren J Isaacs in:

  9. Search for Sriram Kosuri in:

  10. Search for David Baker in:

  11. Search for Stanley Fields in:

  12. Search for George M Church in:

  13. Search for Srivatsan Raman in:

Contributions

N.D.T., F.J.I., G.M.C. and S.R. conceived the study. N.D.T., S.F., G.M.C. and S.R. designed experiments. N.D.T., A.S.G. and S.R. performed experiments and carried out bioinformatic studies. R.M. and D.B. generated computational protein design candidates. S.C., D.C., M.A.A. and S.K. solved the crystal structure of a sucralose-binding variant. S.K. helped with Agilent OLS chip library design. J.K.R. helped optimize screening protocols. N.D.T., A.S.G., S.F., G.M.C. and S.R. analyzed the data. N.D.T., A.S.G., S.F., G.M.C. and S.R. wrote the paper.

Competing interests

S.R., N.D.T. and G.M.C. have filed a patent application (PCT/US15/16868) covering biosensor design methods.

Corresponding author

Correspondence to Srivatsan Raman.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–10, Supplementary Tables 1–6 and Supplementary Note

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmeth.3696

Further reading