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 GABAA 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 GABAA 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.