Optimized 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 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.