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Synthetic microbial sensing and biosynthesis of amaryllidaceae alkaloids
Open accessPreprint — published as 10.1038/s41467-024-46356-y · Paper page
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 low-yielding plants, including the wild daffodil Narcissus pseudonarcissus . Engineered biocatalytic methods have the potential to stabilize the supply chain of amaryllidaceae alkaloids. Here, we propose a highly efficient biosensor-AI technology stack for biocatalyst development, which we apply to engineer amaryllidaceae alkaloid production in Escherichia coli . Directed evolution is used to develop a highly sensitive (EC 50 = 20 uM) and specific biosensor for the key amaryllidaceae alkaloid branchpoint 4-O’Methylnorbelladine. A machine learning model (MutComputeX) was subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which were rapidly screened with the biosensor. Functional enzyme variants were identified that yielded a 60% improvement in product titer, 17-fold reduced remnant substrate, and 3-fold lower off-product regioisomer formation.
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Metadata
- Authors
- d’Oelsnitz, S., Diaz, D., Acosta, D., Schechter, M., Minus, M., Howard, J., Loy, J., Do, H., Alper, H. S., & 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
- openRxiv
- Published
- 2023-04-05
- Volume
- 15
- Issue
- 1
- Pages
- 2084-2084
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10920890
- OpenAlex
- W4392549818
- Cited by
- 58
- References
- 1
- Citations by year
- 2026: 16 · 2025: 18 · 2024: 21 · 2023: 3
- FWCI
- 7.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- 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
- National Science Foundation
- U.S. Department of Defense (FA95501410089)
- U.S. Department of Energy
- U.S. Department of Health and Human Services (R35GM148356)
- U.S. Department of Commerce (70NANB21H100)
- National Institutes of Health
- National Institute of Standards and Technology (70NANB21H100)
- National Institute of General Medical Sciences (R35GM148356)
- National Institute of Biomedical Imaging and Bioengineering (R01EB026533)
- Air Force Office of Scientific Research (FA9550‐14‐1‐0089)
- Lawrence Berkeley National Laboratory
- 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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