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Combined Quantitative and Qualitative Statistical Analyses Improve Benzodiazepine Target Discovery in Label-free

Lydia H Griggs¹, Doo Nam Kim¹, Damon T Leach¹

¹Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.

Journal of Proteome Research

|March 14, 2026

Summary

We developed chemoprotR, an R package for analyzing affinity-based protein profiling (AfBPP) data, and used it to identify benzodiazepine drug targets in the brain, revealing novel protein interactions.

Keywords:

affinity-based protein profiling benzodiazepine chemoproteomics software

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Area of Science:

Proteomics
Neuroscience
Pharmacology

Background:

Affinity-based protein profiling (AfBPP) identifies protein targets of small molecules.
Label-free AfBPP can yield data with high missingness, complicating analysis.
Benzodiazepine interactions extend beyond GABA_A receptors, but targets are not fully understood.

Purpose of the Study:

To develop an R package, chemoprotR, for quantitative and qualitative analysis of AfBPP data.
To identify specific brain protein targets of benzodiazepines using AfBPP.
To evaluate the utility of benzodiazepine affinity-based probes (AfBPs) and statistical analysis for target identification.

Main Methods:

Developed the R package chemoprotR for chemoproteomic data analysis.
Synthesized benzodiazepine AfBPs and applied them to rat brain synaptosomes.
Utilized competitive labeling with flurazepam and analyzed presence-absence data.

Main Results:

Identified GABA_A receptor subunits and other ion channel proteins as benzodiazepine targets.
FR-DA, a flurazepam-based probe, yielded more significant targets than flunitrazepam-based probes.
Demonstrated effective target identification using AfBPs and competitive binding assays.

Conclusions:

chemoprotR facilitates robust analysis of AfBPP data, including challenging datasets with missing values.
Benzodiazepine AfBPs successfully identified known and potential novel protein targets in the brain.
Combined statistical approaches are crucial for interpreting AfBPP data and discovering drug-protein interactions.