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Zenkner, M.

Publications and source records attributed to Zenkner, M..

3 recordsLinked to original sources

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↗

A proteomics analysis of 5xFAD mouse brain regions reveals the lysosome-associated protein Arl8b as a candidate biomarker for Alzheimer's disease

BackgroundAlzheimers disease (AD) is characterized by the accumulation of amyloid-{beta} (A{beta}) peptides in intra- and extracellular deposits. How A{beta} aggregates perturb the proteome in brains of patients and AD transgenic mouse models, however, remains largely unclear. State-of-the-art mass spectrometry (MS) methods can comprehensively detect proteomic alterations in neurodegenerative disorders, providing relevant insights unobtainable with transcriptomics investigations. Analyses of the relationship between progressive A{beta} aggregation and protein abundance changes in brains of 5xFAD transgenic mice have not been reported previously. MethodsWe quantified progressive A{beta} aggregation in hippocampus and cortex of 5xFAD mice and controls with immunohistochemistry and biochemical membrane filter assays. Protein changes in different mouse tissues were analysed by MS-based proteomics using label-free quantification (LFQ); resulting MS data were processed using an established pipeline. Results were contrasted with existing proteomic data sets from postmortem AD patient brains. Finally, abundance changes in the candidate marker Arl8b were validated in CSF from AD patients and controls using ELISAs. ResultsExperiments revealed a more rapid accumulation of A{beta}42 peptides in hippocampus than in cortex of 5xFAD mice, accompanied by many more protein abundance changes in hippocampus than in cortex, indicating that A{beta}42 aggregate deposition is associated with brain region-specific proteome perturbations. Generating time-resolved data sets, we defined A{beta} aggregate-correlated and anticorrelated proteome changes, a fraction of which was conserved in postmortem AD patient brain tissue, suggesting that proteome changes in 5xFAD mice mimic disease relevant changes in human AD. We detected a positive correlation between A{beta}42 aggregate deposition in the hippocampus of 5xFAD mice and the abundance of the lysosome-associated small GTPase Arl8b, which accumulated together with axonal lysosomal membranes in close proximity of extracellular A{beta} plaques in 5xFAD brains. Abnormal aggregation of Arl8b was observed in AD brain tissue. Arl8b protein levels were significantly increased in cerebrospinal fluid (CSF) of AD patients, a clinically accessible body fluid. ConclusionsWe report a comprehensive biochemical and proteomic investigation of hippocampal and cortical brain tissue derived from 5xFAD transgenic mice, providing a valuable resource to the neuroscientific community. We identified Arl8b, with significant abundance changes in 5xFAD and AD patient brains. Arl8b might enable the measurement of progressive lysosome accumulation in AD patients and have clinical utility as a candidate biomarker. Data are available via ProteomeXchange with identifier PXD030348.

molecular biology↗

A quantitative mapping approach to identify direct interactions within complexomes

Complementary methods are required to fully characterize all protein complexes, or the complexome, of a cell. Affinity purification coupled to mass-spectrometry (AP-MS) can identify the composition of complexes at proteome-scale. However, information on direct contacts between subunits is often lacking. In contrast, solving the 3D structure of protein complexes can provide this information, but structural biology techniques are not yet scalable for systematic, proteome-wide efforts. Here, we optimally combine two orthogonal high-throughput binary interaction assays, LuTHy and N2H, and demonstrate that their quantitative readouts can be used to differentiate direct interactions from indirect associations within multiprotein complexes. We also show that LuTHy allows accurate distance measurements between proteins in live cells and apply these findings to study the impact of the polyglutamine expansion mutation on the structurally unresolved N-terminal domain of Huntingtin. Thus, we present a new framework based on quantitative interaction assays to complement structural biology and AP-MS techniques, which should help to provide first-approximation contact maps of multiprotein complexes at proteome-scale. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/457734v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1cc7cf1org.highwire.dtl.DTLVardef@1eed9borg.highwire.dtl.DTLVardef@8197b2org.highwire.dtl.DTLVardef@e5cb8_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗