Martin J. Weissenborn

This paper introduces MLEE, a machine learning-assisted enzyme engineering workflow that improves hit rates for high-fitness enzyme variants compared to traditional methods.

The paper designs a panel of selective peroxygenases for terpene oxyfunctionalization using computational methods, resulting in improved enzymatic functions and new products.

The paper maps the substrate preferences and selectivity of 15 UPOs using 16 substrates, revealing high chemo- and regioselectivity and turnover numbers.

The paper develops a promoter and signal peptide shuffling system in Pichia pastoris to improve heterologous production of fungal peroxygenases and other enzymes.

This paper develops a modular yeast secretion system for producing and applying fungal peroxygenases on a preparative scale.

The paper introduces Golden Mutagenesis, a method for creating mutagenesis libraries with automated primer design, enabling rapid and cost-effective multi-site saturation mutagenesis.