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Optimized Automated Workflow for BioID Improves Reproducibility and Identification of Protein-Protein Interactions.

Emilio CirriHannah KnaudtDomenico Di FraiaNadine PömpnerNorman RahnisIvonne HeinzeAlessandro OriTherese Dau
Published in: Journal of proteome research (2024)
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 not only allows 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 the analysis throughput and enable reproducible protein quantitation across a large number of samples. We successfully applied this workflow to optimize the detection of proteasome substrates by proximity-dependent labeling.
Keyphrases
  • electronic health record
  • liquid chromatography
  • machine learning
  • deep learning
  • high throughput
  • induced apoptosis
  • ms ms
  • cell cycle arrest
  • big data
  • oxidative stress
  • cell death
  • artificial intelligence
  • protein protein