Development of a Microfluidics-Based Screening Assay for the High-Throughput Directed Evolution of Artificial Metalloenzymes

Vallapurackal, Jaicy. Development of a Microfluidics-Based Screening Assay for the High-Throughput Directed Evolution of Artificial Metalloenzymes. 2021, Doctoral Thesis, University of Basel, Faculty of Science.


Official URL: https://edoc.unibas.ch/94945/

Downloads: Statistics Overview


The present PhD thesis summarizes the scientific work conducted in the research group of Prof. Dr. Thomas R. Ward during the years 2016–2021.
Research in the Ward group is focused on the development and optimization of artificial metalloenzymes with non-natural activities. These hybrid catalysts, resulting from an incorporation of a metal–containing cofactor within a protein or DNA scaffold, and can be optimized by either chemical or genetic means.
The main part of this thesis deals with the genetic optimization of such systems and the development of higher throughput screening assays to facilitate the process. First attempts dealt with the development of a selection-based assay relying on the Carroll rearrangement (Chapter 2.6). Following, more high-throughput assays such as screening of cells relying on a fluorescent reporter protein (Chapter 3) or the screening of activity by an agar plate screening assay were pursued (Chapter 4.2).
The main part of the thesis focuses on the method development of an ultrahigh-throughput screening platform for the in vivo directed evolution of artificial metalloenzymes using droplet microfluidics. The combination of ArMs and droplet microfluidics, can be a powerful tool for propelling directed evolution-based research forward. Systematic and high-throughput screening of ArMs in vivo using double emulsions could allow the screening of a much bigger sequence space, which is, to date, challenging. Identifying cooperative effects to improve catalysis or even remodelling whole enzymes to achieve new-to-nature reactivities are only two potential examples. Reactions based on ArMs could ultimately provide aqueous, environmentally friendly reaction pathways for industrial applications. Additionally, such big data sets could also be used as an input for machine learning applications, to further study active site plasticity, reaction pathways, or even protein-folding mechanisms.
The developed method was then applied to libraries of different types and sizes, and recent findings of these screenings are highlighted in the fourth chapter.
During the time in the research group of Prof. Dr. Ward, a deeper knowledge in molecular biology, especially library design, high-throughput screening using different approaches, microfluidic method development and fluorescence activated cell sorting (FACS), and the use of different sequencing techniques was garnered.
Advisors:Ward, Thomas R. R.
Committee Members:Seebeck, Florian Peter and Roelfes, Gerard
Faculties and Departments:05 Faculty of Science > Departement Chemie > Chemie > Bioanorganische Chemie (Ward)
05 Faculty of Science > Departement Chemie > Chemie > Molecular Bionics (Seebeck)
UniBasel Contributors:Seebeck, Florian Peter
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:15045
Thesis status:Complete
Number of Pages:XV, 195
Identification Number:
  • urn: urn:nbn:ch:bel-bau-diss150459
edoc DOI:
Last Modified:23 Jun 2023 04:30
Deposited On:22 Jun 2023 12:03

Repository Staff Only: item control page