TargetSeeker-MS: A Bayesian Inference Approach for Drug-Target Discovery Using Protein Fractionation Coupled to Mass...

Area of Science:

• Proteomics
• Pharmacology
• Computational Biology

Background:

• Determining drug-target affinity is crucial for understanding drug mechanisms and clinical viability.
• Energetics-based protein separation coupled with mass spectrometry (EBPS-MS) shows promise for proteome-wide drug target identification.
• Current computational analyses for EBPS-MS are often protocol-specific, limiting their broad applicability.

Purpose of the Study:

• To develop a flexible computational approach for identifying drug targets across diverse EBPS-MS techniques.
• To introduce TargetSeeker-MS, a novel Bayesian inference method for robust drug-target prediction.
• To validate the efficacy and reproducibility of TargetSeeker-MS in identifying drug targets.

Main Methods:

• Developed TargetSeeker-MS, a flexible Bayesian inference computational approach.
• Applied TargetSeeker-MS to analyze proteomic data from *Caenorhabditis elegans* and HEK 293 cells treated with benomyl.
• Validated identified drug targets through in vitro enzymatic activity assays.

Main Results:

• TargetSeeker-MS successfully identified known and novel benomyl drug targets in both *C. elegans* and HEK 293 samples.
• Drug-target identifications by TargetSeeker-MS were reproducible across different EBPS techniques (thermal shift assay and DiffPOP) in *C. elegans*.
• A novel benomyl target was experimentally validated by demonstrating altered enzymatic activity upon drug treatment.

Conclusions:

• TargetSeeker-MS provides a versatile and confident method for proteome-scale drug target identification.
• The approach enhances understanding of drug mechanisms of action.
• TargetSeeker-MS facilitates the evaluation of drug clinical viability and facilitates drug discovery.