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Engineering an efficient and enantioselective enzyme for the Morita–Baylis–Hillman reaction

Nature Chemistry volume 14, pages 313–320 (2022)Cite this article

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Abstract

The combination of computational design and directed evolution could offer a general strategy to create enzymes with new functions. So far, this approach has delivered enzymes for a handful of model reactions. Here we show that new catalytic mechanisms can be engineered into proteins to accelerate more challenging chemical transformations. Evolutionary optimization of a primitive design afforded an efficient and enantioselective enzyme (BH32.14) for the Morita–Baylis–Hillman (MBH) reaction. BH32.14 is suitable for preparative-scale transformations, accepts a broad range of aldehyde and enone coupling partners and is able to promote selective monofunctionalizations of dialdehydes. Crystallographic, biochemical and computational studies reveal that BH32.14 operates via a sophisticated catalytic mechanism comprising a His23 nucleophile paired with a judiciously positioned Arg124. This catalytic arginine shuttles between conformational states to stabilize multiple oxyanion intermediates and serves as a genetically encoded surrogate of privileged bidentate hydrogen-bonding catalysts (for example, thioureas). This study demonstrates that elaborate catalytic devices can be built from scratch to promote demanding multi-step processes not observed in nature.

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Fig. 1: A designed enzyme for the MBH reaction and the development of a dual-function mechanistic inhibitor.
Fig. 2: Characterization of BH32, BH32.14 and selected variants.
Fig. 3: Substrate scope of engineered MBHases.
Fig. 4: Crystal structures of BH32 and BH32.12.
Fig. 5: Proposed catalytic mechanism of an engineered MBHase.

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Data availability

Coordinates and structure factors have been deposited in the Protein Data Bank under accession numbers 6Z1K, 7O1D and 6Z1L. Data supporting the findings of this study are available within the paper and its Supplementary Information. Source data are provided with this paper.

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Acknowledgements

We acknowledge the Biotechnology and Biological Sciences Research Council (David Phillips Fellowship BB/M027023/1 to A.P.G.), the European Research Council (ERC Starter Grant no. 757991 to A.P.G.) and the UK Research and Innovation Council (Future Leader Fellowship MR/T041722/1 to S.L.L.). We thank the Faculty of Science and Engineering (University of Manchester) for the award of a Presidential Fellowship to S.L.L. A.E.C. was supported by a BBSRC Industrial CASE PhD studentship (BB/S507040/1) supported by GSK. We are grateful to the Diamond Light Source for time on beamlines i03 and i04 under proposals MX17773–33 and MX17773–74, to the Manchester SYNBIOCHEM Centre (BB/M017702/1), the Future Biomanufacturing Hub (EP/S01778X/1) and the Henry Royce Institute for Advanced Materials (funded through EPSRC grants nos. EP/R00661X/1, EP/S019367/1, EP/P025021/1 and EP/P025498/1) for access to their facilities, and to M. Dunstan (Manchester Institute of Biotechnology) for guidance on automating directed evolution workflows. We thank R. Spiess and R. Sung (Manchester Institute of Biotechnology) for acquiring protein mass spectra and for assistance with HPLC method development, and Reach Separations (Nottingham) for supplying individual enantiomers of MBH adduct 3. We acknowledge assistance given by IT Services and use of the Computational Shared Facility at the University of Manchester.

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Authors and Affiliations

  1. Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester, UK

    Rebecca Crawshaw, Amy E. Crossley, Linus Johannissen, Ashleigh J. Burke, Sam Hay, Colin Levy, Sarah L. Lovelock & Anthony P. Green

  2. Department of Biochemistry, University of Washington, Seattle, WA, USA

    David Baker

  3. Institute for Protein Design, University of Washington, Seattle, WA, USA

    David Baker

  4. Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

    David Baker

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  1. Rebecca Crawshaw
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Contributions

R.C. carried out molecular biology, protein production, purification, crystallization and kinetic characterization, and directed evolution experiments. A.E.C. and R.C. carried out organic synthesis and substrate profiling of the BH32 variants. R.C., A.J.B. and A.E.C. developed spectrophotometric assays and performed enzyme-inhibition experiments. L.J. and S.H. carried out molecular-docking and DFT calculations. S.H. interpreted and analysed kinetic data. C.L. interpreted, analysed and presented structural data. D.B. provided the BH32 design model. All authors discussed the results and participated in writing the manuscript. A.P.G. and S.L.L. directed the research.

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Correspondence to Sarah L. Lovelock or Anthony P. Green.

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The authors declare no competing interests.

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Peer review information Nature Chemistry thanks Andrew Buller, Elaine O’Reilly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–13, Tables 1–9, DNA and protein sequences, Cartesian coordinates for energy-minimized cluster models.

Supplementary Data 1

Source data for Supplementary Figs. 2, 5, 6, 11 and 13.

Supplementary Data 2

Raw NMR data for Supplementary Fig. 4a.

Supplementary Data 3

Raw NMR data for Supplementary Fig. 4b.

Source data

Source Data Fig. 1

Raw data supporting the main text and Fig. 1b.

Source Data Fig. 2

Raw data supporting the main text and Figs. 2b, 2c and 2d.

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Crawshaw, R., Crossley, A.E., Johannissen, L. et al. Engineering an efficient and enantioselective enzyme for the Morita–Baylis–Hillman reaction. Nat. Chem. 14, 313–320 (2022). https://doi.org/10.1038/s41557-021-00833-9

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