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

ABSTRACT: Determining the affinity of a drug to its putative targets is critical to the understanding of its mechanism and to assess its clinical usefulness and viability. Energetics-based protein separation (EBPS) techniques, such as thermal shift assay, when coupled to mass spectrometry (MS) 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 rudimentary and tightly tied to the protocol under which the data were produced. We propose a novel flexible Bayesian inference approach named TargetSeeker-MS to identify drug targets in datasets produced using different EBPS techniques coupled to MS. We show that TargetSeeker-MS identifies known and novel drug targets in C. elegans and HEK 293 samples treated with benomyl, a fungicide. We also demonstrate 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 selective precipitation-based separation). In addition, we validate a novel benomyl target by measuring its altered enzymatic activity upon drug treatment in vitro. TargetSeeker-MS, which is available as a web server, allows for the rapid and confident identification of targets of a drug on a proteome scale, thereby providing a better understanding of its mechanisms to ease the evaluation of its clinical viability.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human) Caenorhabditis Elegans

TISSUE(S): Cell Culture, Whole Body

SUBMITTER: James Moresco  

LAB HEAD: John R. Yates III

PROVIDER: PXD010799 | Pride | 2026-04-06

REPOSITORIES: Pride