Markus Jeschek

This study enhances malonyl-CoA availability in Pseudomonas putida through multi-level optimization, achieving a 5.8-fold increase in phloroglucinol production.

This paper improves enzyme engineering by combining active learning and experimental screening to efficiently discover high-performing artificial metalloenzymes.

The paper uses ultradeep sequence-function mapping to analyze 1.2 million 5-UTR-CDS pairs in E. coli, refuting the rare-codon hypothesis and revealing base-pairing between initiator tRNA and mRNA.

This paper introduces a systematic platform for engineering artificial metalloenzymes to enhance their catalytic activity for new-to-nature reactions.

The paper introduces DNA-based phenotypic recording to generate large-scale sequence-function datasets and uses deep learning to predict gene regulatory element function with high accuracy.