Adrian Bachmann

This paper develops a phage-assisted evolution platform to engineer allosteric protein switches, achieving near-binary optogenetic switches with high dynamic range.

This paper engineers thermo-responsive allosteric proteins using temperature-sensitive domains, enabling precise control of protein activity in both bacterial and mammalian systems.

This paper introduces ProDomino, a machine learning tool for predicting domain insertion sites in proteins, validated experimentally in E. coli and human cells to create opto- and chemogenetic switches.

The paper introduces COBRA, an interpretable deep learning model for scRNA-seq data that disentangles covariate effects, and demonstrates its application on interferon-stimulated mouse datasets.