An Automated Scalable Chemoproteomics Workflow that Recovers Known Serine Hydrolase Target Profiles of Clinically Used Drugs

Abstract

Activity-based protein profiling has established itself as a powerful technology with demonstrated use in on- and off-target profiling. However, determining the off-target profile of many drugs using the unbiased mass spectrometry-based chemoproteomics approach remains time- and cost-intensive. In this study, we implemented an SP3-based, high-throughput, plate-based chemoproteomic workflow compatible with liquid-handling robotics. We then profiled a library of 40 marketed drugs targeting the serine hydrolases or containing potentially reactive electrophiles against the human serine hydrolase proteome in HT29 cell lysate. We included two additional well-studied literature compounds as controls to benchmark our workflow. Our results validated 16 out of 20 intended human targets and found 5 previously described off-targets for the compounds tested. Additionally, we discovered six unknown off-targets that could warrant future follow-up research. In all, our approach demonstrates how automated chemoproteomics can be used to accelerate the identification of clinically relevant off-targets, providing a scalable blueprint for rational drug repurposing.