PhD student
Group of Prof. Erich Bornberg-Bauer, Institute for Evolution and Biodiversity, University of Münster
Contact email: b.eenink@uni-muenster.de
ES-Cat start date: 1st April 2017

Background:
I studied MSc Molecular Life Sciences with a specialization in Biological Chemistry at the University of Wageningen and obtained my Master’s degree in 2017. During my Master Thesis in the group of Prof. Dr. Willem van Berkel I worked on the expression, purification and characterization of a flavin dependent monooxygenase of with an unknown substrate. During my Master I also did a 6-month internship in the group of Miguel Alcalde at the CSIC in Madrid, Spain. I worked on the directed evolution of a ligninolytic manganese peroxidase (EC 1.11.1.13) towards functional expression in yeast.

Training and Transferable Skills:

  • Molecular biology methods (S. cerevisiae, E. coli)
  • Generation of mutant libraries
  • Screening of mutant libraries
  • Protein expression and purification (AKTÄ)
  • Enzyme characterization

Research Projects:
Directed evolution in the lab can emulate natural evolution, which over a course of a few billion years has resulted in a wide variety of protein structures carrying out many functions with unrivalled efficiency. However, the combination of mutations needed for large improvements cannot always be reached due to the occurrence of general loss of function or epistatic ratchets. To avoid evolutionary dead ends knowledge of the fitness landscape around proteins would be helpful.
Several ancestral sequences of the alkaline phosphatase (AP) superfamily have been inferred, reconstructed and their substrate specificity profiles have been mapped and nodes were function diverged have been identified. These substrate specificity profiles suggest a charge in primary function that is the result of a shift in specificity rather than de novo invention of a novel activity.
The main aim of my project is to explore the sequence space around ancestral and extant members of the AP superfamily using directed evolution approaches, connecting their ‘fitness parameters’, i.e. the interplay of activity, stability and epistasis to the shape of the fitness landscape of their local sequence space. This should provide quantitive insight into which properties are most important in determining the evolvability of enzymes.