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Engineering an allosteric transcription factor to respond to new ligands

Nature Methods volume 13, pages 177–183 (2016)Cite this article

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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.

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Figure 1: General workflow for designing new ligand binding in an aTF.
Figure 2: Characterization of Rosetta-designed variants responding to new inducers.
Figure 3: Characterization of gentiobiose-responsive variants from the protein-wide single-amino-acid substitution library.
Figure 4: Ligand cross-reactivity of LacI variants.
Figure 5: Activity maturation of LacI.
Figure 6: Crystal structure and GFP induction with ligand of sucralose-binding LacI design variant (D149T,S193D,V150A,I156L).

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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.

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Author notes

  1. Rocco Moretti & Srivatsan Raman

    Present address: 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.).,

Authors and 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

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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.

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Correspondence to Srivatsan Raman.

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

Integrated supplementary information

Supplementary Figure 1 Chemical structure of ligands.

Allolactose and IPTG (native and synthetic inducers of LacI, respectively), and the four new inducers – fucose, lactitol, sucralose and gentiobiose.

Supplementary Figure 2 Coverage of all single-amino-acid substitutions found in the pre-selection single-site saturation mutagenesis library at gentiobiose-responsive positions.

Residues are ordered by protein region as in Supplementary Fig. 8 for comparison. (a) For each position of wild-type LacI, 19 substitutions were synthesized for the single amino-acid substitution library. By next-generation sequencing we measured how many of the 19 possible substitutions were found either before or after negative selection for positions showing response to gentiobiose. Mutants missing from the input library are likely due to synthesis or cloning inefficiencies. (b) Most (>80%) of the positions involved in gentiobiose response were found to have at least 18 of 19 single amino-acid substitutions prior to positive selection. For all 360 positions of LacI (not shown), we found 195 (~54%) positions contained all 19 substitutions, 238 (~66%) contained at least 18, and 306 (85%) contained at least 14 substitutions.

Source data

Supplementary Figure 3 Flow cytometric characterization of aTF library screening.

RFU denotes relative fluorescence units. (a) Flow cytometry histogram of representative LacI variant library (red) before and (blue) after colicin E1 negative selection. (b) Flow cytometry histogram of representative LacI variant library with no inducer molecule present (blue) or exposed to a new target inducer molecule (red).

Supplementary Figure 4 Fold induction of WT LacI with IPTG inducer.

Dose-response curve of WT LacI with IPTG, fold induction shown on the y-axis and IPTG concentration (mM) on the x-axis.

Supplementary Figure 5 Sequence and fold induction of the top-scoring full-length Rosetta design variants.

Induction response in relative fluorescence units (RFU) with and without ligand for WT LacI and top five scoring full-length Rosetta design variants for sucralose, lactitol and fucose. WT LacI was induced with IPTG, and the full-length Rosetta design variants were induced with their respective target ligands. The mutations in each Rosetta design variant are shown above the bar graph. All ligands were supplemented at 10 mM.

Source data

Supplementary Figure 6 Comparison of fucose, lactitol and sucralose response versus single-amino-acid substitutions found after negative selection.

(a) Fucose responsive induction values are shown pink. The induction values show the maximum weighted fold-change of response after positive selection. The black outlines indicate depletion of next-generation sequencing reads for single amino-acid substitutions after negative selection. The depletion value is the log2 fold-change of reads prior to negative selection divided by the read counts after negative selection. Higher depletion values indicate position and side-chain combinations that are lost after negative selection. Read counts were quantile normalized between pre- and post-selection separately for each amplicon (see Online Methods). (b) Lactitol responsive induction values versus depletion values. (c) Sucralose responsive induction values versus depletion values. Negative depletion values are not shown.

Source data

Supplementary Figure 7 Comparison of conservation of amino acids and mutations found for fucose response.

Mutations conferring fucose response in LacI are shown as red outlines. (a) A set of 41 LacI orthologs were aligned and the frequency of amino acid utilization is shown in blue. (b) Five experimentally validated sequences of GalR/S known to bind fucose were aligned with E. coli LacI and shown with respect to LacI positions. Mutations at positions 79 and 273 overlap with preferentially conserved amino acids in the GalR set, shown with arrows. The highest inducer at position 291 was conserved in neither LacI or fucose-responding GalR/S.

Source data

Supplementary Figure 8 Comparison of gentiobiose response versus single-amino-acid substitutions found after negative selection.

Induction values for gentiobiose-responding mutants are shown in pink. The induction values show the maximum weighted fold-change of response after positive selection. The color shades outside the amino acid substitution profile denotes the location of the residue in the binding pocket, dimerization interface, DNA-binding domain or as unclassified. The black outlines indicate depletion of next-generation sequencing reads for single amino-acid substitutions. The depletion value is the log2 fold-change of reads prior to negative selection divided by the read counts after negative selection. Read counts were quantile normalized between pre- and post-selection separately for each amplicon (see Online Methods).

Supplementary Figure 9 Cross-reactivity of additional LacI variants toward three other untargeted inducers and IPTG.

For additional variants displayed in Fig. 2, a dose-response was determined for non-target ligands and IPTG. Values displayed represent the highest fold induction at any ligand concentration. Inducers are colored as follows: gentiobiose, red; fucose, green; lactitol, blue; sucralose, magenta; and IPTG, black. Variants displayed were designed for binding to (a) gentiobiose, (b) fucose, (c) lactitol, and (d) sucralose. Error bars represent standard deviation of fold induction from three biological replicates.

Supplementary Figure 10 User guide for aTF redesign.

A detailed flowchart that guides the user through the choice of mutagenesis methods based on the choice of the target ligand for aTF redesign. We offer general guidelines on what we consider acceptable fold induction and specificity values by target and native ligands, presented as proportion of WT aTF induction, after the two-stage enrichment screen and following activity maturation. These guidelines could be adjusted on a case-by-case basis depending on the number and quality of ligand-responsive variants after the two-stage enrichment screen, and the nature of downstream application.

Supplementary information

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Supplementary Figures 1–10, Supplementary Tables 1–6 and Supplementary Note (PDF 1962 kb)

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Taylor, N., Garruss, A., Moretti, R. et al. Engineering an allosteric transcription factor to respond to new ligands. Nat Methods 13, 177–183 (2016). https://doi.org/10.1038/nmeth.3696

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