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Enzyme Cascade Design: Retrosynthesis Approach

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Enzyme Cascade Design and Modelling

Abstract

Retrosynthetic analysis for the design of synthetic routes towards target molecules is well-established in organic chemistry, and has been extended to include biocatalysis in recent years. The increasing number of transformations known to be catalysed by enzymes, whilst ultimately rendering biocatalytic retrosynthesis more powerful, necessitates the use of computational tools if biocatalysis is to reach its full potential. In the following chapter, we outline the pipeline required to go from pathway generation towards a target molecule, to construction of selected optimal pathways in the laboratory and the techniques currently used to analyse them. We compare manual vs. computer-assisted approaches for each step of the workflow. Current computational tools used for automated identification of suitable enzymes, such as molecular fingerprinting and structure-based substrate docking, and the evaluation of metrics that can be used to rank order the generated pathways, will also be discussed. Finally, we discuss a number of recent high-throughput analytical techniques for the experimental validation of potential pathways, leveraging the design-build-test-analyse cycle for pathway improvement.

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Authors and Affiliations

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

    William Finnigan, Sabine L. Flitsch, Lorna J. Hepworth & Nicholas J. Turner

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  1. William Finnigan
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  2. Sabine L. Flitsch
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  3. Lorna J. Hepworth
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  4. Nicholas J. Turner
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Correspondence to Nicholas J. Turner .

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Editors and Affiliations

  1. Department of Biological and Chemical Engineering, Aarhus University, Aarhus, Denmark

    Selin Kara

  2. Institute of Applied Synthetic Chemistry, TU Wien, Vienna, Austria

    Florian Rudroff

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Finnigan, W., Flitsch, S.L., Hepworth, L.J., Turner, N.J. (2021). Enzyme Cascade Design: Retrosynthesis Approach. In: Kara, S., Rudroff, F. (eds) Enzyme Cascade Design and Modelling. Springer, Cham. https://doi.org/10.1007/978-3-030-65718-5_2

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