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Biology subjects

Lisurek, M.

Publications and source records attributed to Lisurek, M..

2 recordsLinked to original sources

Active Site-Directed Probes for targeting Bacterial Phosphoarginine Phosphatases

Canonical protein phosphorylation patterns are a thoroughly studied post-translational modification (PTM) driving distinct regulatory mechanisms in both prokaryotes, and eukaryotes. In contrast, the identification and investigation of essential components that regulate non-canonical phosphorylation has received considerably less attention, although these PTMs are associated with important functions. One notable example is arginine phosphorylation which modulates processes such as protein degradation, transcriptional regulation and spore germination in bacteria. Herein we introduce the first in class covalent activity-based probes to study phosphoarginine-phosphatases. We identify unsaturated phosphonamidic acids as bespoke electrophilic phosphoarginine (pArg) mimics, which allowed to uncover a series of unprecedented pArg-phosphatases, which in part had been previously annotated as low molecular weight tyrosine-phosphatases across phylogenetically distinct microbial species. This work, which serves as the first example of proteome-wide activity-based profiling of pArg phosphatases will help inform the development of new therapeutic modalities and expand our understanding of bacterial signal transduction.

microbiology↗

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

systems biology↗