Screening protein sequences to discover novel protein functions using informatics target selection and ultrahigh-throughput droplet microfluidics

使用信息学靶点选择和超高通量液滴微流体筛选蛋白质序列以发现新的蛋白质功能

• 批准号: 2273624
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2273624
• 关键词: Screening; protein; sequences; discover; novel

PhD project strategic theme: Biosciences for renewable resources and clean growthBacterial communities provide potential as a great source for biocatalysts, however many of them are unculturable and therefore potentially novel and useful biocatalysts remain undetected. Additionally, because desired activities of novel biocatalysts do not necessarily share similar sequences, sequence analysis can not predict many novel hits. Functional screening using microfluidic picodroplet compartments using bait substrates can identify these novel proteins.Droplet microfluidics is a technique in which single cells are compartmentalised in water oil emulsions which flow in microchannels. A number of modules have been created in the literature to expand the workflow of the microfluidic devices including flow focusing devices, picoinjection devices, and absorbance activated cell sorting devices. The benefits of droplet microfluidics include that it can facilitate ultrahigh-throughput experiments (can sort based on absorbance readout of greater than 1 million droplets per hour) which is needed to pick out rare variants, it requires minimum amount of reagents, and droplets can be manipulated independently, serving as individual reaction vessels.The focus will be on exploiting metagenomic data to discover novel enzymes by using droplet microfluidics. It will be a synergy between computation and experimental approaches enable cross-comparison and bridge the wet lab/in silico divide. Current microfluidic modules will be iterated on to allow greater automation of the microfluidic workflow process. Machine learning methods will be used to better characterise the large amount of experimental data gained. By performing rapid experiments in picolitre reaction volumes the computation methods will be refined by iterative cycles of wet/dry work, and array based gene synthesis will be exploited to allow access to all predications, in order to discover novel biocatalysts.

PHD项目战略主题：可再生资源和清洁生长的生物科学细菌群落为生物催化剂提供了巨大的来源，但其中许多细菌是不可培养的，因此潜在的新的和有用的生物催化剂仍未被发现。此外，由于新生物催化剂的预期活性不一定具有相似的序列，因此序列分析不能预测许多新的命中。使用使用诱饵底物的微流控微滴隔室进行功能筛选可以识别这些新的蛋白质。滴状微流控是一种将单个细胞在流动在微通道中的水-油乳剂中隔间的技术。在文献中已经创建了许多模块来扩展微流控设备的工作流程，包括流动聚焦设备、微微注射设备和吸光度激活细胞分选设备。液滴微流体的优点包括它可以促进超高通量实验(可以基于每小时超过100万个液滴的吸光度读数进行分类)，这是挑选稀有变体所需的，它需要最少的试剂，并且液滴可以作为单独的反应容器独立操作。重点将是利用元基因组数据通过使用液滴微流体来发现新的酶。这将是计算和实验方法之间的协同作用，能够进行交叉比较，并弥合湿实验室/硅技术的鸿沟。目前的微流控模块将被重复使用，以实现微流控工作流程的更大自动化。机器学习方法将被用来更好地描述获得的大量实验数据。通过在皮升反应体积中进行快速实验，计算方法将通过湿/干工作的迭代循环进行改进，并将利用基于阵列的基因合成来实现所有预测，以发现新的生物催化剂。