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 Caenorhabditis elegans and HEK 293 samples treated with the fungicide benomyl. We also demonstrated that TargetSeeker-MS' drug–target identifications are reproducible in C. elegans 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.