Screening protein sequences to discover novel protein functions using informatics target selection and ultrahigh-throughput droplet microfluidics
Lead Research Organisation:
University of Cambridge
Department Name: Biochemistry
Abstract
PhD project strategic theme: Biosciences for renewable resources and clean growth
Bacterial 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.
Bacterial 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.
