Synbio production of monoterpenes through computationally-guided enzyme engineering

单萜类化合物（C10类异戊二烯）是一大类结构多样的天然化合物，其作为调味剂、香料和燃料在工业上具有吸引力。单萜由单一线性底物香叶基二磷酸通过一组称为单萜环化酶/脱氢酶（mTC/Ss）的酶产生，所述酶催化涉及不稳定碳阳离子中间体的高能环化反应。由于mTC/Ss的高度支化反应机制的性质，通过生物催化或代谢工程生产单萜的努力通常导致形成多种产物。我们已经在曼彻斯特合成生物学研究综合体（SYNBIOCHEM; ChemistrySelect）中使用自动化合成生物学菌株生物工程方法建立了用于生产这些化合物的平台微生物菌株。2016，1，1893-），并使用多学科方法（包括酶生物化学，结构生物学和计算化学）发现和表征了一系列新的mTC/S酶（ACS Catal.2017，7，6268-）。该项目将专注于优化mTC/S酶和体内代谢途径。（细菌）生物合成特定的感兴趣的化合物;最初，蒎烯，桉叶醇和芳樟醇，在与其他行业合作伙伴协商后，有范围扩大这种选择。使用计算方法设计变体的迭代方法，然后使用我们建立的mTC/S测定进行实验表征。最初的工作将集中在现有的大型且不断增长的mTC/S序列库和在曼彻斯特整理的产品配置文件上，该库将用于基准计算预测和训练机器学习算法。

Monoterpenes (C10 isoprenoids) are a large group of structurally diverse natural compounds that are attractive to industry as flavours, fragrances and fuels. Monoterpenes are produced from a single linear substrate, geranyl diphosphate, by a group of enzymes called the monoterpene cyclases/synthases (mTC/Ss) that catalyse high-energy cyclisation reactions involving unstable carbocation intermediates. Efforts towards producing monoterpenes via biocatalysis or metabolic engineering often result in the formation of multiple products due to the nature of the highly branched reaction mechanism of mTC/Ss. We have established platform microbial strains for the production of these compounds using automated synthetic biology strain bioengineering approaches in the Manchester Synthetic Biology Research Complex (SYNBIOCHEM; ChemistrySelect. 2016, 1, 1893-) and have discovered and characterised a range of new mTC/S enzymes using a multidisciplinary approach including enzyme biochemistry, structural biology and computational chemistry (ACS Catal. 2017, 7, 6268-).This project will focus on the optimisation of mTC/S enzymes and pathways for the in vivo (bacterial) biosynthesis of specific compounds of interest; initially, pinene, cineol and linalool with scope to expand this selection after consultation with additional industry partners. An iterative approach using computational methods to design variants that are then characterised experimentally using our established mTC/S assays. Initial work will focus on an existing large, and ever-growing library of mTC/S sequences and product profiles collated in Manchester, which will be used for benchmarking computational predictions and to train machine-learning algorithms.