Chemoproteomics

Chemoproteomics (also known as chemical proteomics) entails a broad array of techniques used to identify and interrogate protein-small molecule interactions. Chemoproteomics complements phenotypic drug discovery, a paradigm that aims to discover lead compounds on the basis of alleviating a disease phenotype, as opposed to target-based drug discovery (reverse pharmacology), in which lead compounds are designed to interact with predetermined disease-driving biological targets. As phenotypic drug discovery assays do not provide confirmation of a compound's mechanism of action, chemoproteomics provides valuable follow-up strategies to narrow down potential targets and eventually validate a molecule's mechanism of action. Chemoproteomics also attempts to address the inherent challenge of drug promiscuity in small molecule drug discovery by analyzing protein-small molecule interactions on a proteome-wide scale. A major goal of chemoproteomics is to characterize the interactome of drug candidates to gain insight into mechanisms of off-target toxicity and polypharmacology. Chemoproteomics assays can be stratified into three basic types. Solution-based approaches involve the use of drug analogs that chemically modify target proteins in solution, tagging them for identification. Immobilization-based approaches seek to isolate potential targets or ligands by anchoring their binding partners to an immobile support. Derivatization-free approaches aim to infer drug-target interactions by observing changes in protein stability or drug chromatography upon binding. Computational techniques complement the chemoproteomic toolkit as parallel lines of evidence supporting potential drug-target pairs, and are used to generate structural models that inform lead optimization. Several targets of high profile drugs have been identified using chemoproteomics, and the continued improvement of mass spectrometer sensitivity and chemical probe technology indicates that chemoproteomics will play a large role in future drug discovery.