Matthias Geyer

This study shows that NLRP3 directly senses intracellular potassium ions, with K+ levels regulating its conformation and inflammasome activation.

This paper combines evolutionary algorithms with AI to design potent Nectin-4-specific cancer biologics, achieving high-affinity minibinders and functional reagents.

The paper uses mathematical modeling to show P-TEFb phosphorylates CTD repeats with local cooperativity but no directional bias.

This study identifies the C-terminal lysine residues of NLRP10 as critical for its localization to lipid compartments and oligomer formation, highlighting unique activation mechanisms compared to other NLRPs.

This paper characterizes BAL-0028, a novel NLRP3 inhibitor with a distinct binding site and mechanism, showing potent inhibition of human and primate NLRP3 and efficacy in a peritonitis model.

This paper identifies and characterizes BAL-1516, a CNS-penetrating NLRP3 inhibitor that blocks inflammasome formation and shows potential for treating neuroinflammatory diseases.

This study shows NLRP3 promotes homologous recombination by interacting with REV7, enhancing DNA repair and affecting PARP inhibitor sensitivity.

This paper develops AI-designed protein binders for cancer cell surface proteins, showing their effectiveness in detection and targeting through experimental validation and benchmarking of structure prediction models.

This study identifies Aurora-A as a CDK12-activating kinase in neuroblastoma, showing its role in transcription termination and tumor growth suppression.

This study identifies the SAVED domain of CalpL as a ring nuclease that cleaves cA4, revealing its role in regulating the CRISPR defense system.

This study reports the crystal structure of fully activated Cdk7/Cyclin H/Mat1 complex with dual T-loop phosphorylations, revealing their distinct roles in kinase activity and substrate recognition.

The paper identifies two distinct NLRP3 activation pathways, one dependent on decameric cage formation and another independent of the MTOC and TGN.

This study uses single-cell RNA-seq and computational methods to analyze macrophage states in COPD patients, revealing transcriptional changes and metabolic shifts associated with the disease.