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

Nature Protocols volume 13, pages 752–767 (2018)Cite this article

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Abstract

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.

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Figure 1: Chemical proteomics workflow with inhibitor and probes used in this study.
Figure 2: Results of the competitive ABPP experiment in mice with DH376 and the probe cocktail, with hierarchical clustering of probe targets.
Figure 3: Protein and peptide abundance data for selected probe targets.

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Acknowledgements

We thank U. Distler and S. Tenzer for their advice and helpful discussions. We acknowledge ChemAxon for kindly providing the Instant JChem software to manage our compound library. This work was supported by grants from the Chinese Scholarship Council (to H.D. and J.Z.) and a Dutch Research Council–Chemical Sciences ECHO grant (to M.v.d.S.).

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Authors and Affiliations

  1. Department of Molecular Physiology, Leiden Institute of Chemistry, Leiden University, Leiden, the Netherlands

    Eva J van Rooden, Hui Deng, Marc P Baggelaar, Annelot C M van Esbroeck, Juan Zhou & Mario van der Stelt

  2. Department of Bioorganic Synthesis, Leiden Institute of Chemistry, Leiden University, Leiden, the Netherlands

    Bogdan I Florea & Herman S Overkleeft

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  1. Eva J van Rooden
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Contributions

E.J.v.R. designed and performed experiments, analyzed data and wrote the paper; B.I.F., H.D. and J.Z. treated the mice; B.I.F. performed experiments and wrote the paper; M.P.B. and A.C.M.v.E. developed the initial protocol, and H.S.O and M.v.d.S. wrote the paper.

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Correspondence to Mario van der Stelt.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Successful methanol/chloroform precipitation.

Photos of a successfully performed methanol/chloroform precipitation (Steps 14-21).

Supplementary Figure 2 Relative activity of the target enzymes in each tissue treated with DH376 compared to vehicle control.

Error bars are ratio of error (calculated as described in step 66 of the protocol).

Supplementary Figure 3 Putative probe targets identified in mouse brain membrane proteome.

Venn diagram of putative probe targets identified in mouse brain membrane proteome with THL-biotin (blue), FP-biotin (red) or a mix of both (cocktail; purple).

Supplementary Figure 4 Relative activity of the target enzymes in each tissue treated with DH376 compared to vehicle control.

Mean + standard deviation is shown.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 (PDF 877 kb)

Life Sciences Reporting Summary (PDF 141 kb)

Supplementary Data 1

Protein sequence database. (ZIP 6017 kb)

Supplementary Data 2

Quantified proteins. (ZIP 1381 kb)

Supplementary Data 3

Quantified peptides. (ZIP 195 kb)

Supplementary Data 4

Putative probe targets. (ZIP 2 kb)

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van Rooden, E., Florea, B., Deng, H. et al. Mapping in vivo target interaction profiles of covalent inhibitors using chemical proteomics with label-free quantification. Nat Protoc 13, 752–767 (2018). https://doi.org/10.1038/nprot.2017.159

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