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Knaudt, H.

Publications and source records attributed to Knaudt, H..

2 recordsLinked to original sources

Structural basis for the ubiquitin chain recognition of the human 26S proteasome

Proteasomal degradation is a fundamental process for all eukaryotic life. A protein destined for degradation is first tagged with a polyubiquitin chain, which is selected by the proteasome. Different ubiquitin chain topologies serve as distinct signals, with K48-linked chains acting as the canonical degradation signal and K11/K48-branched chains providing even more potent targeting, particularly during cell cycle regulation. However, the structural basis for how the proteasome distinguishes between these different chain architectures has remained unclear. Here, we present high-resolution cryo-EM structures of the human 26S proteasome bound to both a K48-linked tetraubiquitin chain and a K11/K48-branched chain. Our structures reveal distinct binding modes for these two types of chain linkage. K48 chains wrap around the Ubiquitin interaction motif of the receptor RPN10 in an unexpected spiral conformation, while K11 branches engage the proteasome through previously uncharacterised interfaces in a cleft formed between RPN2 and RPN10. Through structure-guided mutagenesis and cellular studies, we demonstrate that these binding modes are essential for efficient substrate degradation and cell cycle progression. These findings establish how the proteasome achieves selective substrate recognition through chain topology-specific interactions.

biochemistry↗

Automated workflow for BioID improves reproducibility and identification of protein-protein interactions

Proximity dependent biotinylation is an important method to study protein-protein interactions in cells, for which an expanding number of applications has been proposed. The laborious and time consuming sample processing has limited project sizes so far. Here, we introduce an automated workflow on a liquid handler to process up to 96 samples at a time. The automation does not only allow higher sample numbers to be processed in parallel, but also improves reproducibility and lowers the minimal sample input. Furthermore, we combined automated sample processing with shorter liquid chromatography gradients and data-independent acquisition to increase analysis throughput and enable reproducible protein quantitation across a large number of samples. We successfully applied this workflow to optimise the detection of proteasome substrates by proximity-dependent labelling.

biochemistry↗