Andreas Tholey

This paper introduces a workflow that decouples quantification and identification in label-free top-down proteomics, improving proteome coverage and quantification precision.

This paper identifies AvrStb6 as a fungal effector that protects Zymoseptoria tritici from antagonistic wheat-associated bacteria by associating with the fungal cell wall.

This paper introduces a 2D nanoLCxCZE-MS platform for deep top-down proteomics, significantly increasing proteoform identification in human colon cancer samples.

This study identifies a functional lysozyme in placozoans, showing its role in acidic extracellular digestion and tracing its evolutionary origin to a bacterial horizontal gene transfer.

The paper compares phagocytic processing of native and foreign Vibrio isolates in sponges, finding differential cellular and proteomic responses.

The paper characterizes the small protein MtrR in Methanosarcina mazei, showing it modulates Mtr activity in response to hydrogen, affecting growth under varying H2 conditions.

This study shows that deleting HKDC1 in intestinal epithelial cells reduces tumor growth and protects against colorectal cancer in mice by impairing mitochondrial function and cell proliferation.

FLASHQuant is a fast, accurate, and open-source algorithm for label-free quantification of proteoforms in top-down proteomics, resolving co-eluting signals efficiently.

This study shows that the membrane toxin TisB in E. coli causes protein aggregation, which enhances cellular dormancy and antibiotic tolerance.

The paper identifies Zymoseptoria tritici effectors that suppress plant immune responses by screening libraries and analyzing structural predictions.

The paper develops a method to measure enzymatic parameters in vivo using synthetic biology tools and applies it to study carotenoid pathway enzymes in yeast.

This study uses proteomics to compare C. elegans responses to two protective Pseudomonas symbionts, identifying distinct host pathways involved in pathogen resistance and tolerance.

The paper develops PIA-M, a multimodal transformer-based tool for predicting HLA-II peptide presentation, achieving higher accuracy and faster runtime than existing methods.