Biosketch

Dr. Max Fürst

Max Fürst studied Biology at the University of Munich (LMU) and obtained his Master degree in 2013. He then joined the group of Marco Fraaije at GBB as a PhD student, where he engineered enzymes for biocatalysis. After his graduation (cum laude) in 2019, Max went to the Medical Research Council Laboratory of Molecular Biology in Cambridge for a postdoc with Prof. Phil Holliger. There, his research was supported by an EMBO postdoctoral fellowship and focussed on the development of ultra-high-throughput screens for protein engineering and directed evolution. In 2022, Max obtained an NWO Veni grant and moved back to Groningen to start a tenure-track assistant professor position at the Groningen Biomolecular Sciences & Biotechnology Institute (GBB). His main research interest lies in the combination of computational design and high-throughput screens in protein engineering for synthetic biology. A core question in his research is how a large number of computationally predicted protein variants can be efficiently tested experimentally. To that end, his lab applies an interdisciplinary approach involving: computational biology / bioinformatics, molecular / synthetic biology, and biochemistry.

Three top publications 2017-2022

1. Fürst MJLJ, Boonstra M, Bandstra S & Fraaije MW (2019) Stabilization of cyclohexanone monooxygenase by computational and experimental library design. Biotechnology and Bioengineering 116 (9): 2167-2177; DOI: https://doi.org/10.1002/bit.27022

A protein engineering study on an industrially-relevant oxidative biocatalyst showcasing the power of combined computational mutation predictions and plate-based activity screens.

2. Fürst MJLJ, Romero E, Gómez Castellanos JR, Fraaije MW & Mattevi A (2019) Side-chain pruning has limited impact on substrate preference in a promiscuous enzyme. ACS Catalysis 12 (8): 11648-11656; DOI: https://doi.org/10.1021/acscatal.8b03793

An example of how protein engineering is used to understand the intriguing mechanisms of enzyme catalysis. We show that active-site residues in cofactor-containing enzymes converting hydrophobic substrates are far less important than previously thought.

3. Korbeld KT & Fürst MJLJ (2023) Curse and blessing of non-proteinogenic parts in computational enzyme engineering. ChemBioChem: e202300192; DOI: https://doi.org/10.1002/cbic.202300192

The first publication of the Fürst lab is a guide for successful computational protein engineering of enzymes with non-proteinogenic parts, such as cofactors, metals, or non-canonical amino acids.