Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme
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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-46356-y |
| _version_ | 1797266719574589440 |
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| author | Simon d’Oelsnitz Daniel J. Diaz Wantae Kim Daniel J. Acosta Tyler L. Dangerfield Mason W. Schechter Matthew B. Minus James R. Howard Hannah Do James M. Loy Hal S. Alper Y. Jessie Zhang Andrew D. Ellington |
| author_facet | Simon d’Oelsnitz Daniel J. Diaz Wantae Kim Daniel J. Acosta Tyler L. Dangerfield Mason W. Schechter Matthew B. Minus James R. Howard Hannah Do James M. Loy Hal S. Alper Y. Jessie Zhang Andrew D. Ellington |
| author_sort | Simon d’Oelsnitz |
| collection | DOAJ |
| description | 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 (EC50 = 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. |
| first_indexed | 2024-04-25T01:05:10Z |
| format | Article |
| id | doaj.art-a590be639471443d8f68f7c532e9fc11 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-25T01:05:10Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-a590be639471443d8f68f7c532e9fc112024-03-10T12:16:18ZengNature PortfolioNature Communications2041-17232024-03-0115111410.1038/s41467-024-46356-yBiosensor and machine learning-aided engineering of an amaryllidaceae enzymeSimon d’Oelsnitz0Daniel J. Diaz1Wantae Kim2Daniel J. Acosta3Tyler L. Dangerfield4Mason W. Schechter5Matthew B. Minus6James R. Howard7Hannah Do8James M. Loy9Hal S. Alper10Y. Jessie Zhang11Andrew D. Ellington12Department of Molecular Biosciences, University of Texas at AustinDepartment of Chemistry, University of Texas at AustinMcKetta Department of Chemical Engineering, University of Texas at AustinDepartment of Molecular Biosciences, University of Texas at AustinDepartment of Molecular Biosciences, University of Texas at AustinDepartment of Molecular Biosciences, University of Texas at AustinDepartment of Chemistry, Prairie View A&M University, 100 University DrDepartment of Chemistry, University of Texas at AustinDepartment of Molecular Biosciences, University of Texas at AustinDepartment of Molecular Biosciences, University of Texas at AustinMcKetta Department of Chemical Engineering, University of Texas at AustinDepartment of Molecular Biosciences, University of Texas at AustinDepartment of Molecular Biosciences, University of Texas at AustinAbstract 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 (EC50 = 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.https://doi.org/10.1038/s41467-024-46356-y |
| spellingShingle | Simon d’Oelsnitz Daniel J. Diaz Wantae Kim Daniel J. Acosta Tyler L. Dangerfield Mason W. Schechter Matthew B. Minus James R. Howard Hannah Do James M. Loy Hal S. Alper Y. Jessie Zhang Andrew D. Ellington Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme Nature Communications |
| title | Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme |
| title_full | Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme |
| title_fullStr | Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme |
| title_full_unstemmed | Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme |
| title_short | Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme |
| title_sort | biosensor and machine learning aided engineering of an amaryllidaceae enzyme |
| url | https://doi.org/10.1038/s41467-024-46356-y |
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