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Using fungible biosensors to evolve improved alkaloid biosyntheses

Nature Chemical Biology volume 18, pages 981–989 (2022)Cite this article

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Abstract

A key bottleneck in the microbial production of therapeutic plant metabolites is identifying enzymes that can improve yield. The facile identification of genetically encoded biosensors can overcome this limitation and become part of a general method for engineering scaled production. We have developed a combined screening and selection approach that quickly refines the affinities and specificities of generalist transcription factors; using RamR as a starting point, we evolve highly specific (>100-fold preference) and sensitive (half-maximum effective concentration (EC50) < 30 μM) biosensors for the alkaloids tetrahydropapaverine, papaverine, glaucine, rotundine and noscapine. High-resolution structures reveal multiple evolutionary avenues for the malleable effector-binding site and the creation of new pockets for different chemical moieties. These sensors further enabled the evolution of a streamlined pathway for tetrahydropapaverine, a precursor to four modern pharmaceuticals, collapsing multiple methylation steps into a single evolved enzyme. Our methods for evolving biosensors enable the rapid engineering of pathways for therapeutic alkaloids.

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Fig. 1: Screening identifies a biosensor responsive to BIAs.
Fig. 2: The SELIS approach for biosensor evolution.
Fig. 3: Evolution of highly specific BIA sensors from a generalist template.
Fig. 4: Crystal structures of evolved biosensors bound to cognate BIAs.
Fig. 5: Unique molecular adaptations confer alkaloid specificity.
Fig. 6: Evolved biosensor enables a new THP-biosynthetic pathway.

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Data availability

The coordinates for the complex structures have been deposited in the PDB: RamR in complex with berberine, PDB 3VW2; PAP4 in complex with PAP, PDB 7N53; ROTU4 in complex with ROTU, PDB 7N4W; NOS4 in complex with NOS, PDB 7N4Z; GLAU4 in complex with GLAU, PDB 7N54. Protein sequence information was retrieved from the NCBI database: RamR, 3VVX_A; TtgR, WP_010952495.1; QacR, WP_001807342.1; SmeT, WP_014648459.1; NalD, WP_003092152.1; Bm3R1, WP_013083972.1; GfOMT1, AKO60152.1. Plasmid sequences relevant to this study (Supplementary Notes 1 and 2) were deposited in Addgene. Source data are provided with this paper.

Code availability

Code used to generate bar plots, dose–response functions and orthogonality matrices presented in this text is accessible at https://github.com/simonsnitz/plotting.

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Acknowledgements

Funding from DARPA Soils (HR00111920019 to A.D.E.), Welch (F-1654 to A.D.E.) and AFSOR (FA9550-14-1-0089 to H.S.A. and A.D.E.) is acknowledged. This work is partially supported by grants from the National Institutes of Health (R01GM104896 and R01GM125882 to Y.Z.). We thank K.J. Blake in the Chemistry Department at the University of Texas at Austin for performing LC–MS analysis and S. Kar for his thoughtful advice on selection circuit design.

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Authors and Affiliations

  1. Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA

    Simon d’Oelsnitz, Nathaniel T. Burkholder, Kamyab Javanmardi, Yan Zhang & Andrew D. Ellington

  2. McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA

    Wantae Kim & Hal S. Alper

  3. Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, USA

    Ross Thyer

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  1. Simon d’Oelsnitz
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Contributions

S.d.O. designed the experiments and performed biosensor evolution and characterization. S.d.O. and R.T. performed protein purification. Enzyme evolution was carried out by S.d.O. and K.J., and X-ray crystallography was conducted by W.K. and N.T.B. The manuscript was written by S.d.O. with support from A.D.E., R.T., Y.Z. and H.S.A. S.d.O., A.D.E. and H.S.A. supervised all aspects of the study.

Corresponding authors

Correspondence to Simon d’Oelsnitz or Andrew D. Ellington.

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Competing interests

S.d.O., K.J., R.T. and A.D.E. have filed two patent applications on materials described in this text. R.T. and A.D.E have equity in GRO Biosciences, a company developing protein therapeutics. The other authors declare no conflict of interest.

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Nature Chemical Biology thanks Michael Jensen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Background fluorescence measurements for all RamR generations.

The same promoter was used to express variants from each evolutionary trajectory (see methods). Individual fluorescent measurements for each condition performed in biological triplicate are shown alongside error bars that represent the SE ± the mean.

Source data

Extended Data Fig. 2 Cross reactivity of all evolved sensors.

Fold-response is shown for all BIAs for the native RamR protein, the first, second, third, and fourth generations from top to bottom, respectively. 100 μM of the indicated BIA was applied in all conditions. Measurements for each condition represent the average of three biological replicates.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–21, Tables 1–3 and Notes 1 and 2

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Source Data Fig. 1

Contains .XLSX sheets with statistical source data.

Source Data Fig. 3

Contains .XLSX sheets with statistical source data.

Source Data Fig. 6

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Source Data Extended Data Fig. 1

Contains .XLSX sheets with statistical source data.

Source Data Extended Data Fig. 2

Contains .XLSX sheets with statistical source data.

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d’Oelsnitz, S., Kim, W., Burkholder, N.T. et al. Using fungible biosensors to evolve improved alkaloid biosyntheses. Nat Chem Biol 18, 981–989 (2022). https://doi.org/10.1038/s41589-022-01072-w

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