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Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries

Nature Biotechnology volume 39, pages 630–641 (2021)Cite this article

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Abstract

Current methods used for measuring amino acid side-chain reactivity lack the throughput needed to screen large chemical libraries for interactions across the proteome. Here we redesigned the workflow for activity-based protein profiling of reactive cysteine residues by using a smaller desthiobiotin-based probe, sample multiplexing, reduced protein starting amounts and software to boost data acquisition in real time on the mass spectrometer. Our method, streamlined cysteine activity-based protein profiling (SLC-ABPP), achieved a 42-fold improvement in sample throughput, corresponding to profiling library members at a depth of >8,000 reactive cysteine sites at 18 min per compound. We applied it to identify proteome-wide targets of covalent inhibitors to mutant Kirsten rat sarcoma (KRAS)G12C and Bruton’s tyrosine kinase (BTK). In addition, we created a resource of cysteine reactivity to 285 electrophiles in three human cell lines, which includes >20,000 cysteines from >6,000 proteins per line. The goal of proteome-wide profiling of cysteine reactivity across thousand-member libraries under several cellular contexts is now within reach.

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Fig. 1: SLC-ABPP using minimal sample input, reduced instrument time and TMT sample multiplexing.
Fig. 2: Benchmarking proteome-wide SLC-ABPP using lead compounds with known targets.
Fig. 3: High-throughput screening of a small-molecule, fragment-based electrophilic library using SLC-ABPP.
Fig. 4: Small-molecule electrophiles can display high specificity for their protein targets.
Fig. 5: Proof-of-concept rescreening of 285 compound electrophiles in two additional cell lines.
Fig. 6: Selective and specific SRC engagement of p-loop cysteine (C-280) by a small-molecule electrophile.

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Data availability

The mass spectrometry data have been deposited at the ProteomeXchange Consortium with the dataset identifier PXD022511. Source data are provided with this paper. SLCAPP data generated during this study are also avaliable using the viewer on the Gygi lab website (https://gygi.hms.harvard.edu/resources.html).

Code availability

The RTS Comet functionality has been released and is available at http://comet-ms.sourceforge.net/. Real-time access to spectral data was enabled by the Thermo Scientific Fusion API (https://github.com/thermofisherlsms/iapi).

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Acknowledgements

We thank the members of the Gygi laboratory for fruitful discussions about this work. We thank A. Reed for assistance with mouse experiments. We thank F. Ferguson, G. Du and N. Gray for providing SM-71 and TL13-68 for SRC experiments. This work was supported in part through a sponsored research agreement with Google Ventures and Third Rock Ventures and grants from the NIH (nos. GM67945 to S.P.G., CA231991 to B.F.C. and CA217809 to E.T.K.), Dana-Farber Cancer Institute Claudia Adams Barr Program for Innovative Cancer Research Award and the Hale Family Center for Pancreatic Cancer Research (to J.D.M.).

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  1. Devin K. Schweppe

    Present address: Department of Genome Sciences, University of Washington, Seattle, WA, USA

Authors and Affiliations

  1. Department of Cell Biology, Harvard Medical School, Boston, MA, USA

    Miljan Kuljanin, Dylan C. Mitchell, Devin K. Schweppe, David P. Nusinow, Nathan J. Bulloch & Steven P. Gygi

  2. Division of Radiation and Genome Stability, Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Miljan Kuljanin, Ajami S. Gikandi & Joseph D. Mancias

  3. Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA

    Ekaterina V. Vinogradova & Benjamin F. Cravatt

  4. Department of Chemistry, Stanford Cancer Institute and ChEM-H Institute, Stanford University, Stanford, CA, USA

    David L. Wilson & Eric T. Kool

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Contributions

M.K., D.C.M., A.S.G. and E.V.V. prepared samples for proteomic analysis. M.K., D.C.M. and D.K.S. collected the proteomics datasets. D.K.S. implemented the Orbiter search strategy. D.K.S., N.J.B. and D.P.N. built the community resource website. D.P.N. performed data interpretation and skewness calculations. M.K. and D.C.M. synthesized and purified desthiobiotin. D.L.W. and E.T.K. synthesized the MGMT-specific probe, NR-1. E.V.V. and B.F.C. provided the scout fragments and performed the ibrutinib mouse study. M.K., D.C.M., D.K.S., D.P.N., J.D.M., B.F.C. and S.P.G. interpreted the results. M.K. and S.P.G. conceived the project, improved the workflow and wrote the manuscript.

Corresponding author

Correspondence to Steven P. Gygi.

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Competing interests

B.F.C. is a founder and scientific advisor of Vividion Therapeutics. S.P.G. is a member of the scientific advisory boards of Thermo Fisher Scientific, Cell Signaling Technology and Casma Therapeutics. S.P.G. is a founder of Cedilla Therapeutics and a scientific advisor to Third Rock Ventures. All other authors declare no competing interests.

Additional information

Peer review information Nature Biotechnology thanks Marcus Bantscheff and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–7.

Reporting Summary

Supplementary Table 1

CR for cells treated with increasing concentrations of ARS16-20.

Supplementary Table 2

CR for cells treated with increasing concentrations of THZ1.

Supplementary Table 3

CR for mice treated with increasing concentrations of ibrutinib.

Supplementary Table 4

CR for cell lysates treated with scout fragments.

Supplementary Table 5

Information on compounds used in large electrophile library screening.

Supplementary Table 6

CR for HCT116 cells treated with electrophile library.

Supplementary Table 7

CR for HEK293T cells treated with electrophile library.

Supplementary Table 8

CR for PaTu-8988T cells treated with electrophile library.

Supplementary Table 9

Whole-proteome raw data and protein differences after treatment with CL71.

Supplementary Data

Synthesis scheme for generation of desthiobiotin-IA, including hrMS2 and NMR.

Source data

Source Data Fig. 1

Uncropped immunoblot for data used in Fig. 6e.

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Kuljanin, M., Mitchell, D.C., Schweppe, D.K. et al. Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries. Nat Biotechnol 39, 630–641 (2021). https://doi.org/10.1038/s41587-020-00778-3

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