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Research GBB Biotransformation and Biocatalysis

Biotransformation and Biocatalysis

Group leaders: Prof. Dr. Marco Fraaije and Dr. Maximilian Fürst

The Biotransformation and Biocatalysis unit comprises two groups of Molecular Enzymology. The two group leaders, Prof. Dr. Marco Fraaije and Assistant Professor Dr. Maximilian Fürst, conduct research aimed at obtaining insight into the biology of enzymatic transformation of natural and synthetic compounds, and the development of improved enzymes and proteins applied in biocatalysis and synthetic biology.

While the Fraaije group’s expertise lies in the elucidation of enzyme mechanisms and the application of this knowledge in enzyme engineering and biocatalytic applications, the Fürst lab is developing novel strategies in computational protein (re)design and developing experimental strategies for high-throughput screening assays.

Prof. Fraaije specializes in uncovering natural enzyme mechanisms and applying this knowledge to engineer biocatalytic solutions, with a particular emphasis on redox enzymes such as monooxygenases, oxidases, and peroxidases. The group studies catalytic mechanisms, kinetic properties, and structure-function relationships in enzymes that can be used for regio- and enantioselective synthesis reactions for preparing useful chemicals. Rational enzyme engineering and directed evolution is employed to modify enzyme properties and enable novel types of chemical transformations. While the research is primarily driven by curiosity, the increasing industrial use of enzymes makes it highly applicable. The group actively participates in private-public partnership projects and its success is evident in the spin-off, GECCO biotech, active in commercial biocatalysis and protein engineering.

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Dr. Fürst's research is dedicated to advancing the field of protein engineering through innovative methods to enhance enzymes important for synthetic biology and biocatalysis. The group employs an interdisciplinary approach that combines computational design techniques, rooted in bioinformatics, biophysical modeling, and machine learning, with experimental exploration. By integrating novel enzymatic high-throughput functional screens with deep sequencing, the group aims to achieve two key goals: 1) discover rare, highly functional enzyme variants, and 2) create extensive sequence-function relationship datasets that illuminate a protein's fitness landscape. These substantial datasets serve as the basis for refining modeling and prediction algorithms, as well as providing input for machine learning-driven design approaches.

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Last modified:23 October 2023 12.29 p.m.