Combined Quantitative and Qualitative Statistical Analyses Improve Benzodiazepine Target Discovery in Label-free Affinity-Based Protein Profiling Data

Affinity-based protein profiling (AfBPP) allows us to identify target proteins that bind drugs or other small molecules of interest in complex samples. As an enrichment technique, label-free AfBPP often generates data with high missingness, particularly in negative control samples. We developed an R package, chemoprotR, which enables both quantitative and qualitative statistical analyses of chemoproteomic data, and applied it to the identification of specific benzodiazepine drug targets in the brain. Benzodiazepines comprise a class of drugs that affect GABA_A receptors through positive allosteric modulation, but benzodiazepine interactions with other proteins are not fully understood. To this end, we synthesized benzodiazepine affinity-based probes (AfBPs) and applied them to rat brain synaptosomes. Our benzodiazepine AfBPs identified GABA_A receptor subunits and other proteins with ion channel functions. Across the three probes, there was minimal overlap in protein targets identified by competitive labeling with flurazepam, and FR-DA, the probe based on flurazepam, yielded more significant protein targets than the probes based on flunitrazepam. These results demonstrate the ability of benzodiazepine AfBPs to identify protein targets when used with an authentic benzodiazepine to compete for binding sites and highlight the utility of combined statistical analyses for the interpretation of presence-absence data in AfBPP data sets.