Mapping in vivo target interaction profiles of covalent inhibitors using chemical proteomics with label-free quantification

Nature Protocols(2018)

Cited 59|Views30
No score
Abstract
This protocol describes the identification of in vivo target interaction profiles of covalent enzyme inhibitors using chemical proteomics. Enzymes are enriched by activity-based probes and quantified by label-free mass spectrometry. Activity-based protein profiling (ABPP) has emerged as a valuable chemical proteomics method to guide the therapeutic development of covalent drugs by assessing their on-target engagement and off-target activity. We recently used ABPP to determine the serine hydrolase interaction landscape of the experimental drug BIA 10-2474, thereby providing a potential explanation for the adverse side effects observed with this compound. ABPP allows mapping of protein interaction landscapes of inhibitors in cells, tissues and animal models. Whereas our previous protocol described quantification of proteasome activity using stable-isotope labeling, this protocol describes the procedures for identifying the in vivo selectivity profile of covalent inhibitors with label-free quantitative proteomics. The optimization of our protocol for label-free quantification methods results in high proteome coverage and allows the comparison of multiple biological samples. We demonstrate our protocol by assessing the protein interaction landscape of the diacylglycerol lipase inhibitor DH376 in mouse brain, liver, kidney and testes. The stages of the protocol include tissue lysis, probe incubation, target enrichment, sample preparation, liquid chromatography–mass spectrometry (LC–MS) measurement, data processing and analysis. This approach can be used to study target engagement in a native proteome and to identify potential off targets for the inhibitor under investigation. The entire protocol takes at least 4 d, depending on the number of samples.
More
Translated text
Key words
Chemical biology,Drug discovery,Mass spectrometry,Proteomics,Target identification,Life Sciences,general,Biological Techniques,Analytical Chemistry,Microarrays,Computational Biology/Bioinformatics,Organic Chemistry
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined