Optimized\nAutomated Workflow for BioID Improves Reproducibility\nand Identification of Protein–Protein Interactions

Proximity-dependent biotinylation is an important method\nto study\nprotein–protein interactions in cells, for which an expanding\nnumber of applications has been proposed. The laborious and time-consuming\nsample processing has limited project sizes so far. Here, we introduce\nan automated workflow on a liquid handler to process up to 96 samples\nat a time. The automation not only allows higher sample numbers to\nbe processed in parallel but also improves reproducibility and lowers\nthe minimal sample input. Furthermore, we combined automated sample\nprocessing with shorter liquid chromatography gradients and data-independent\nacquisition to increase the analysis throughput and enable reproducible\nprotein quantitation across a large number of samples. We successfully\napplied this workflow to optimize the detection of proteasome substrates\nby proximity-dependent labeling.