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JUL 26, 2016 7:30 AM PDT

Automated assays for protein engineering: in vitro and in vivo

Speaker
  • Postdoctoral Researcher, Biocatalysis, Université de Montréal
    Biography
      Dr. Daniela Quaglia, Ph.D., is a Postdoctoral Researcher in Biocatalysis at Université de Montréal in the laboratory of Prof. Joelle Pelletier. Her current project (co-funded by NSERC and the industrial partner DSM) focuses on the use of enzyme engineering to generate improved biocatalysts to be used in the food industry.
      Dr. Quaglia earned her Bachelor's degree in Chemistry and her Master's in Chemistry of Biological Molecules at the University of Florence in Italy. She then pursued her doctorate in Biocatalysis at University College Dublin, in Ireland where her work focused on the study of the enzyme Horse Liver Alcohol Dehydrogenase and its application in the synthesis of anti-inflammatory drugs. She then moved to the UK, where she worked in industry at Synthace, a Synthetic Biology company in London, then in academia, as a Research Associate in Biocatalysis in the laboratory of Prof. Nicholas Turner at the University of Manchester. Dr. Quaglia has also been involved in teaching at undergraduate level both at the Dundalk Institute of Technology (Ireland) and at the Manchester Metropolitan University. Furthermore, she has a passion for scientific communication: she was recently awarded a prestigious NatureJobs writing competition and a UK Biochemical Society outreach award. Her writing is regularly published in widely-read blogs such as the Huffington Post and you can follow her on Twitter (@Dny_Q).

    Abstract

    Enzyme engineering is a powerful technology now widely used in laboratories around the world. The goal is to obtain improved proteins that will serve as better biocatalysts, biosensors or as a tool to understand protein evolution, which is at the core of many societal problem, such as antibiotic resistance. Enzyme engineering entails the initial generation of libraries of mutants that will then be screened for improved properties (for example: better selectivity or specificity towards a substrate of interest, improved stability, etc). Obtaining good quality libraries is becoming routine thanks to the availability of molecular biology tools and computational simulations. The bottleneck of the experiment remains at the screening level. The bigger the generated library, the more stringent the requirement for the high-throughput screen. 
    In our work we tackle both aspects: library generation and screening throughput by using the enzyme Candida antarctica lipase A (Cal-A) as a model. Cal-A has the potential to be applied in the food industry for the hydrolysis of saturated short-chain fatty acids (C4 and C6) vs. saturated C10-C16 fatty acids - a very useful tool for the dairy industry. Upon generation of several libraries of mutants using a novel Golden Gate-based strategy, we developed two screening methods (in vivo and in vitro) that give us the required high-throughput capacity. The two screening methods, one carried out on agar plates and the other in liquid, are completely automated and they are both optimized for our Biomek NXP system.


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