Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
Synthetic Biology HIVE, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.
Nat Commun. 2024 Mar 7;15(1):2084. doi: 10.1038/s41467-024-46356-y.
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 = 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.
实现治疗性生物碱产业化生物制造的一个主要挑战是生物催化剂工程的缓慢进程。石蒜科生物碱，如治疗老年痴呆症的加兰他敏，是具有公认治疗价值的复杂植物次生代谢物。由于它们的合成难度大，通常通过从低产水仙花 Narcissus pseudonarcissus 中提取和纯化来获取。在这里，我们提出了一种用于生物催化剂开发的高效生物传感器-机器学习技术组合，并将其应用于在大肠杆菌中工程化石蒜科酶。定向进化用于开发一种针对关键石蒜科生物碱分支点 4'-O-甲基诺贝林的高灵敏度（EC=20μM）和特异性生物传感器。随后开发了基于结构的残差神经网络（MutComputeX），并用于生成植物甲基转移酶的活性富集变体，然后用生物传感器快速筛选。鉴定出具有功能的酶变体，可使产物滴度提高 60%，催化活性提高 2 倍，副产物区域异构体形成减少 3 倍。解决的晶体结构阐明了关键有益突变背后的机制。
