Matthias Glögl

This study designs genetically encoded miniproteins to modulate FAK kinase activity, showing they can inhibit or activate FAK in vitro and in cells, and adapt them to target other kinases.

Conditioning RFdiffusion on edge beta-strand pairing yields specific binders to polar targets, with in silico success above 9.2% versus 0.98% for hotspot-only conditioning.

The paper designs novel cytokines by fusing computationally generated receptor-binding domains, identifying several that activate signaling pathways and have specific biological effects.

This paper introduces computational methods to design pH-sensitive binders that dissociate under acidic conditions, demonstrating their application in targeting various receptors and proteins.

This paper designs potent small protein inhibitors of C. difficile toxin B using deep learning and Rosetta, showing high affinity and stability for potential clinical use.

This paper presents a computational strategy to design bifaceted protein nanomaterials with two distinct sides, enabling anisotropic functionalization and controlled structural variation.

This study designs miniproteins that inhibit MERS-CoV by targeting the spike protein, showing broad neutralization and protective efficacy in mice.

This study uses target-conditioned diffusion to design high-affinity TNFR superfamily antagonists and agonists with switchable specificity.

The paper designs a potent inhibitor against P. sordellii lethal toxin using deep learning, showing protective effects in mice against toxic shock.