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.

具有生物活性的天然产物具有重要的生物技术、生物医学、环境和经济意义。因此，许多公司和学术团体都对利用微生物衍生的生物合成途径来制造天然产品感兴趣。然而，这些产品的生产和纯化可能是昂贵的，有时令人望而却步，因此最大化途径的生产力是一个主要的优先事项。该项目的总体目标是开发一种计算和实验相结合的策略，以优化微生物生物合成途径的生产力，重点关注通常被认为是安全的（GRAS）生物酿酒酵母，即面包酵母。产生有用的生物活性分子的多种微生物途径可以在遗传水平上导入酵母。然而，这些途径的产量往往很低，这对于希望通过经济上可行的工艺生产有价值产品的公司来说是一个问题。因此，我们希望建立一套如何优化酵母生物合成途径的规则，从而使更广泛的科学和商业社区能够以有效和可预测的方式最大化这些途径的生产力。我们对创建易于使用的实验和计算工具“包”特别感兴趣，这些工具可以被中小型商业实体使用，因为这些实体通常受到用于实施新技术的资源数量的限制。同时，我们相信，我们的整体战略将有可能使任何对高效生产生物活性分子感兴趣的工业或学术团队受益。为了开发和应用我们的方法，我们将专注于一个模型途径，在这种情况下，一个途径，使聚酮分子比卡维林。我们将使用合成的DNA片段组装大量编码比卡维林途径酶的基因簇的变体。一旦将变异引入酵母，将在机器人平台上分析所产生的基因簇变异的性能。这种自动化将大大加快比较不同型号性能的过程。通过上述实验程序获得的分析数据将被输入到一个先进的新计算模型中，从而增强我们对如何最好地最大化毕卡维林途径整体活性的理解。该模型将根据这一策略相应地发展，最终作为洞察如何设计其他生物合成途径以在未来最大化性能的来源。