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

To understand the mechanism of action of a drug and assess its clinical usefulness and viability, it is imperative that its affinity for its putative targets is determined. When coupled to mass spectrometry (MS), energetics-based protein separation (EBPS) techniques, such as a thermal shift assay, have shown great potential to identify the targets of a drug on a proteome scale. Nevertheless, the computational analyses assessing the confidence of drug-target predictions made by these methods have remained tightly tied to the protocol under which the data were produced. To identify drug targets in data sets produced using different EBPS-MS techniques, we have developed a novel flexible Bayesian inference approach named TargetSeeker-MS. We showed that TargetSeeker-MS identifies known and novel drug targets in <i>Caenorhabditis elegans</i> and HEK 293 samples treated with the fungicide benomyl. We also demonstrated that TargetSeeker-MS' drug-target identifications are reproducible in <i>C. elegans</i> samples that were processed using two different EBPS techniques (thermal shift assay and a differential precipitation of proteins, named DiffPOP). In addition, we validated a novel benomyl target by measuring its altered enzymatic activity upon drug treatment in vitro. TargetSeeker-MS, which is available as a web server (https://targetseeker.scripps.edu/), allows for the rapid, versatile, and confident identification of targets of a drug on a proteome scale, thereby providing a better understanding of its mechanisms and facilitating the evaluation of its clinical viability.