Synbio production of monoterpenes through computationally-guided enzyme engineering

单萜烯（C10类异戊二烯）是一大类结构多样的天然化合物，在工业上作为香料、香料和燃料具有吸引力。单萜烯是由一种单一的线性底物，二磷酸香叶基，通过一组称为单萜烯环化酶/合成酶（mTC/Ss）的酶产生的，这些酶催化涉及不稳定碳正离子中间体的高能环化反应。由于mTC/Ss的高支化反应机制，通过生物催化或代谢工程生产单萜烯的努力往往导致形成多种产物。我们已经在曼彻斯特合成生物学研究中心（SYNBIOCHEM; chemstryselect）建立了使用自动化合成生物学菌株生物工程方法生产这些化合物的平台微生物菌株。2016, 1,1893 -)，并使用包括酶生物化学，结构生物学和计算化学在内的多学科方法发现并表征了一系列新的mTC/S酶（ACS catalal . 2017, 7,6268 -）。该项目将专注于优化mTC/S酶和体内（细菌）生物合成特定化合物的途径；最初，蒎烯、桉树醇和芳樟醇在与其他行业合作伙伴协商后扩大了选择范围。使用计算方法设计变量的迭代方法，然后使用我们建立的mTC/S分析进行实验表征。最初的工作将集中在曼彻斯特整理的现有的大型且不断增长的mTC/S序列和产品概况库上，这些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.