Combinatorial Biosynthetic Pathway Engineering

组合生物合成途径工程

• 批准号: EP/X039587/1
• 负责人: John McCarthy
• 金额: $ 114.46万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX039587%2F1
• 关键词: Combinatorial; Biosynthetic; Pathway; Engineering

Bioactive natural products are of great biotechnological, biomedical, environmental and economic importance. For this reason, a large number of companies and academic groups are interested in using biosynthetic pathways derived from microorganisms to manufacture natural products. However, the production and purification of such products can be expensive, sometimes prohibitively so, and therefore maximising the productivity of the pathways is a major priority.The general aim of this project is to develop a combined computational and experimental strategy for optimising the productivity of microbial biosynthetic pathways, focusing on the Generally Regarded As Safe (GRAS) organism S cerevisiae, i.e. baker's yeast. A wide range of microbial pathways that make useful bioactive molecules can be imported at a genetic level into yeast. However, these pathways often have poor yields, which is a problem for a company that wants to manufacture a valuable product via an economically viable process. We therefore want to establish a set of rules how to optimise biosynthetic pathways in yeast, thus enabling the wider scientific and commercial community to maximise the productivity of these pathways in an efficient and predictable way. We are particularly interested in creating easy-to-use 'packages' of experimental and computational tools that can be utilised by small- to medium- sized commercial entities, since these are often constrained in terms of the amount of resources that they can dedicate to implementing new technologies. At the same time, we believe that our overall strategy will potentially benefit any industrial or academic team interested in producing bioactive molecules efficiently. In order to develop and apply our methods, we will focus on a model pathway, in this case a pathway that makes the polyketide molecule bikaverin. We will assemble a very large number of variants of the gene cluster that codes for the bikaverin pathway enzymes using synthetic DNA fragments. The performance of the resulting gene cluster variants will be analysed on a robotic platform once the variants have been introduced into yeast. This automation will greatly accelerate the process of comparing the performance of the different variants. The analytical data obtained through the above experimental procedures will be fed into an advanced new computational model, thus enhancing our understanding of how best to maximise the overall activity of the bikaverin pathway. The model will accordingly evolve as a result of this strategy, ultimately acting as a source of insight into how to design other biosynthetic pathways in order to maximise performance in the future.

生物活性天然产品具有极大的生物技术，生物医学，环境和经济意义。因此，许多公司和学术团体有兴趣使用源自微生物的生物合成途径来制造天然产品。 However, the production and purification of such products can be expensive, sometimes prohibitively so, and therefore maximising the productivity of the pathways is a major priority.The general aim of this project is to develop a combined computational and experimental strategy for optimising the productivity of microbial biosynthetic pathways, focusing on the Generally Regarded As Safe (GRAS) organism S cerevisiae, i.e. baker's yeast.可以将有用的生物活性分子的各种微生物途径在遗传水平中进口到酵母中。但是，这些途径的产量通常很差，这对于想要通过经济上可行的过程生产有价值的产品的公司来说是一个问题。因此，我们希望建立一套规则，如何优化酵母中的生物合成途径，从而使更广泛的科学和商业群落以有效且可预测的方式最大化这些途径的生产率。我们特别有兴趣创建可以由中小型商业实体使用的实验和计算工具的易于使用的“软件包”，因为这些实体通常受到他们可以专门用于实施新技术的资源的限制。同时，我们认为我们的整体战略将有可能使有效生产生物活性分子有效的任何工业或学术团队受益。为了开发和应用我们的方法，我们将专注于模型途径，在这种情况下，一种使聚酮化合物分子bikaverin的途径。我们将使用合成DNA片段组装大量基因簇的变体，这些变体为Bikaverin途径酶编码。一旦将变体引入酵母，将在机器人平台上分析所得基因簇变体的性能。这种自动化将大大加快比较不同变体的性能的过程。通过上述实验过程获得的分析数据将被送入一个高级的新计算模型，从而增强了我们对如何最好地最大程度地提高Bikaverin途径的整体活动的理解。该策略将相应地发展，最终充当了如何设计其他生物合成途径的洞察力来源，以便将来最大程度地提高性能。