Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme

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.

doi:10.1038/s41467-024-46356-y

Title: Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme

Journal Article · 07 March 2024 · Nature Communications

DOI: https://doi.org/10.1038/s41467-024-46356-y · OSTI ID:2471020

^[1];

^[2]; Kim, Wantae ^[2];

^[2]; Dangerfield, Tyler L. ^[2]; Schechter, Mason W. ^[2]; Minus, Matthew B. ^[3]; Howard, James R. ^[2]; Do, Hannah ^[2];

^[2];

^[2]

Harvard Medical School, Boston, MA (United States); Univ. of Texas, Austin, TX (United States)
Univ. of Texas, Austin, TX (United States)
Prairie View A&M University, TX (United States)

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Advanced Light Source (ALS)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); National Institute of Standards and Technology (NIST); US Air Force Office of Scientific Research (AFOSR); National Institutes of Health (NIH)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2471020

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 15; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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