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Cited by 58Enzyme Catalysis and ImmobilizationPlant biochemistry and biosynthesisChemical synthesis and alkaloids

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Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme

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d’Oelsnitz, Simon, Diaz, Daniel J., Kim, Wantae, Acosta, Daniel J., Dangerfield, Tyler L., Schechter, Mason W., Minus, Matthew B., Howard, James R., Do, Hannah, Loy, James M., Alper, Hal S., Zhang, Y. Jessie, Ellington, Andrew D.·2024·Nature Communications
DOI 10.1038/s41467-024-46356-yPMC PMC10920890

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Abstract

A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer’s medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from the low-yielding daffodil Narcissus pseudonarcissus . Here, we propose an efficient biosensor-machine learning technology stack for biocatalyst development, which we apply to engineer an Amaryllidaceae enzyme in Escherichia coli . Directed evolution is used to develop a highly sensitive (EC 50 = 20 μM) and specific biosensor for the key Amaryllidaceae alkaloid branchpoint 4’-O-methylnorbelladine. A structure-based residual neural network (MutComputeX) is subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which are rapidly screened with the biosensor. Functional enzyme variants are identified that yield a 60% improvement in product titer, 2-fold higher catalytic activity, and 3-fold lower off-product regioisomer formation. A solved crystal structure elucidates the mechanism behind key beneficial mutations.

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Metadata

Authors
d’Oelsnitz, S., Diaz, D. J., Kim, W., Acosta, D. J., Dangerfield, T. L., Schechter, M. W., Minus, M. B., Howard, J. R., Do, H., Loy, J. M., Alper, H. S., Zhang, Y. J., & Ellington, A. D.
Affiliations
  • Harvard University (US)
  • Institute for Foundations of Machine Learning (US)
  • The University of Texas at Austin (US)
  • Prairie View A&M University (US)
Corresponding author
d’Oelsnitz, Simon
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2024-03-07
Volume
15
Issue
1
Pages
2084-2084
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-024-46356-y
PMC
PMC10920890
OpenAlex
W4392549818
Cited by
58
References
7
Citations by year
2026: 16 · 2025: 18 · 2024: 21
FWCI
7.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • United States Department of Commerce | National Institute of Standards and Technology (70NANB21H100)
  • United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (FA9550-14-1-0089)
  • U.S. Department of Health & Human Services | National Institutes of Health (R35GM148356)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Plant biochemistry and biosynthesis
  • Physical Sciences › Chemistry › Organic Chemistry › Chemical synthesis and alkaloids
Keywords
Amaryllidaceae alkaloids, galantamine, biosensors, machine learning, directed evolution, methyltransferase, Escherichia coli, biocatalyst engineering, plant secondary metabolites, regioselectivity, crystal structure, bioproduction
MeSH terms
Amaryllidaceae, Alkaloids, Hydrolases, Methyltransferases, Plants, Narcissus, Amaryllidaceae Alkaloids

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