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

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.

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).

References

  1. 1.

    Long, M. J. C. & Aye, Y. Privileged electrophile sensors: a resource for covalent drug development. Cell Chem. Biol. 24, 787–800 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Maurais, A. J. & Weerapana, E. Reactive-cysteine profiling for drug discovery. Curr. Opin. Chem. Biol. 50, 29–36 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Gehringer, M. & Laufer, S. A. Emerging and re-emerging warheads for targeted covalent inhibitors: applications in medicinal chemistry and chemical biology. J. Med. Chem. 62, 5673–5724 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  4. 4.

    Zhang, T., Hatcher, J. M., Teng, M., Gray, N. S. & Kostic, M. Recent advances in selective and irreversible covalent ligand development and validation. Cell Chem. Biol. 26, 1486–1500 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Roberts, A. M., Ward, C. C. & Nomura, D. K. Activity-based protein profiling for mapping and pharmacologically interrogating proteome-wide ligandable hotspots. Curr. Opin. Biotechnol. 43, 25–33 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  6. 6.

    Cravatt, B. F., Wright, A. T. & Kozarich, J. W. Activity-based protein profiling: from enzyme chemistry to proteomic chemistry. Annu. Rev. Biochem. 77, 383–414 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  7. 7.

    Pace, N. J. & Weerapana, E. Diverse functional roles of reactive cysteines. ACS Chem. Biol. 8, 283–296 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. 8.

    Murray, C. W. & Rees, D. C. The rise of fragment-based drug discovery. Nat. Chem. 1, 187–192 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  9. 9.

    Giles, N. M., Giles, G. I. & Jacob, C. Multiple roles of cysteine in biocatalysis. Biochem. Biophys. Res. Commun. 300, 1–4 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  10. 10.

    Bulaj, G., Kortemme, T. & Goldenberg, D. P. Ionization-reactivity relationships for cysteine thiols in polypeptides. Biochemistry 37, 8965–8972 (1998).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. 11.

    Reddie, K. G. & Carroll, K. S. Expanding the functional diversity of proteins through cysteine oxidation. Curr. Opin. Chem. Biol. 12, 746–754 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12.

    Resnick, E. et al. Rapid covalent-probe discovery by electrophile-fragment screening. J. Am. Chem. Soc. 141, 8951–8968 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Gygi, S. P. et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994–999 (1999).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  14. 14.

    Weerapana, E., Speers, A. E. & Cravatt, B. F. Tandem orthogonal proteolysis-activity-based protein profiling (TOP-ABPP) – a general method for mapping sites of probe modification in proteomes. Nat. Protoc. 2, 1414–1425 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  15. 15.

    Weerapana, E. et al. Quantitative reactivity profiling predicts functional cysteines in proteomes. Nature 468, 790–797 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Martell, J. & Weerapana, E. Applications of copper-catalyzed click chemistry in activity-based protein profiling. Molecules 19, 1378–1393 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. 17.

    Weerapana, E., Simon, G. M. & Cravatt, B. F. Disparate proteome reactivity profiles of carbon electrophiles. Nat. Chem. Biol. 4, 405–407 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Backus, K. M. et al. Proteome-wide covalent ligand discovery in native biological systems. Nature 534, 570–574 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Grüner, B. M. et al. An in vivo multiplexed small-molecule screening platform. Nat. Methods 13, 883–889 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  20. 20.

    Matthews, M. L. et al. Chemoproteomic profiling and discovery of protein electrophiles in human cells. Nat. Chem. 9, 234–243 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. 21.

    Bar-Peled, L. et al. Chemical proteomics identifies druggable vulnerabilities in a genetically defined cancer. Cell 171, 696–709 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Senkane, K. et al. The proteome-wide potential for reversible covalency at cysteine. Angew. Chem. Int. Ed. Engl. 58, 11385–11389 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Yang, Y., Hahne, H., Kuster, B. & Verhelst, S. H. L. A simple and effective cleavable linker for chemical proteomics applications. Mol. Cell. Proteomics 12, 237–244 (2013).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  24. 24.

    Qian, Y. et al. An isotopically tagged azobenzene-based cleavable linker for quantitative proteomics. ChemBioChem 14, 1410–1414 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  25. 25.

    Nessen, M. A. et al. Selective enrichment of azide-containing peptides from complex mixtures. J. Proteome Res. 8, 3702–3711 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Rabalski, A. J., Bogdan, A. R. & Baranczak, A. Evaluation of chemically-cleavable linkers for quantitative mapping of small molecule-cysteinome reactivity. ACS Chem. Biol. 14, 1940–1950 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. 27.

    Okerberg, E. S. et al. Identification of a tumor specific, active-site mutation in casein kinase 1α by chemical proteomics. PLoS ONE 11, e0152934 (2016).

  28. 28.

    Rao, S. et al. Leveraging compound promiscuity to identify targetable cysteines within the kinome. Cell Chem. Biol. 26, 818–829 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Zanon, P. R. A., Lewald, L. & Hacker, S. M. Isotopically labeled desthiobiotin azide (isoDTB) tags enable global profiling of the bacterial cysteinome. Angew. Chem. Int. Ed. Engl. 59, 2829–2836 (2020).

  30. 30.

    Zhang, X., Crowley, V. M., Wucherpfennig, T. G., Dix, M. M. & Cravatt, B. F. Electrophilic PROTACs that degrade nuclear proteins by engaging DCAF16. Nat. Chem. Biol. 15, 737–746 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Rauniyar, N. & Yates, J. R. Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293–5309 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Vinogradova, E. V. et al. An activity-guided map of electrophile-cysteine interactions in primary human T cells. Cell 182, 1009–1026 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Erickson, B. K. et al. Active instrument engagement combined with a real-time database search for improved performance of sample multiplexing workflows. J. Proteome Res. 18, 1299–1306 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Schweppe, D. K. et al. Full-featured, real-time database searching platform enables fast and accurate multiplexed quantitative proteomics. J. Proteome Res. 19, 2026–2034 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  35. 35.

    Rauniyar, N. & Yates, J. R. Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13, 5293–5309 (2014).

  36. 36.

    O’Connell, J. D., Paulo, J. A., O’Brien, J. J. & Gygi, S. P. Proteome-wide evaluation of two common protein quantification methods. J. Proteome Res. 17, 1934–1942 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  37. 37.

    Li, J. et al. TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples. Nat. Methods 17, 399–404 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  38. 38.

    Erickson, B. K. et al. Active instrument engagement combined with a real-time database search for improved performance of sample multiplexing workflows. J. Proteome Res. https://doi.org/10.1021/acs.jproteome.8b00899 (2019).

  39. 39.

    Lito, P., Solomon, M., Li, L.-S., Hansen, R. & Rosen, N. Allele-specific inhibitors inactivate mutant KRAS G12C by a trapping mechanism. Science 351, 604–608 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Janes, M. R. et al. Targeting KRAS mutant cancers with a covalent G12C-specific inhibitor. Cell 172, 578–589 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 41.

    Kwiatkowski, N. et al. Targeting transcription regulation in cancer with a covalent CDK7 inhibitor. Nature 511, 616–620 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Browne, C. M. et al. A chemoproteomic strategy for direct and proteome-wide covalent inhibitor target-site identification. J. Am. Chem. Soc. 141, 191–203 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  43. 43.

    Lanning, B. R. et al. A road map to evaluate the proteome-wide selectivity of covalent kinase inhibitors. Nat. Chem. Biol. 10, 760–767 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Haapalainen, A. M. et al. Crystallographic and kinetic studies of human mitochondrial acetoacetyl-CoA thiolase: the importance of potassium and chloride ions for its structure and function. Biochemistry 46, 4305–4321 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  45. 45.

    Davies, T. G. & Hyvönen, M. Fragment-based Drug Discovery and X-ray Crystallography (Springer, 2012).

  46. 46.

    Erlanson, D. A., Davis, B. J. & Jahnke, W. Fragment-based drug discovery: advancing fragments in the absence of crystal structures. Cell Chem. Biol. 26, 9–15 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47.

    Fan, C. H. et al. O6-methylguanine DNA methyltransferase as a promising target for the treatment of temozolomide-resistant gliomas. Cell Death Dis. 4, e876 (2013).

  48. 48.

    Sharma, S. et al. Role of MGMT in tumor development, progression, diagnosis, treatment and prognosis. Anticancer Res. 29, 3759–3768 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Nagel, Z. D. et al. Fluorescent reporter assays provide direct, accurate, quantitative measurements of MGMT status in human cells. PLoS ONE 14, e0208341 (2019).

  50. 50.

    Beharry, A. A., Nagel, Z. D., Samson, L. D. & Kool, E. T. K. Fluorogenic real-time reporters of DNA repair by MGMT, a clinical predictor of antitumor drug response. PLoS ONE 11, e0152684 (2016).

  51. 51.

    Du, G. et al. Structure-based design of a potent and selective covalent inhibitor for SRC kinase that targets a P-loop cysteine. J. Med. Chem. 63, 1624–1641 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Gurbani, D. et al. Structure and characterization of a covalent inhibitor of Src kinase. Front. Mol. Biosci. 7, 81 (2020).

  53. 53.

    Campaner, E. et al. A covalent PIN1 inhibitor selectively targets cancer cells by a dual mechanism of action. Nat. Commun. 8, 15772 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Dubiella, C. et al. Sulfopin, a selective covalent inhibitor of Pin1, blocks Myc-driven tumor initiation and growth in vivo. Preprint at bioRxiv https://doi.org/10.1101/2020.03.20.998443 (2020).

  55. 55.

    Sears, R. C. The life cycle of c-Myc: from synthesis to degradation. Cell Cycle 3, 1131–1135 (2004).

    Article  Google Scholar 

  56. 56.

    Nam, J. et al. Disruption of the Myc-PDE4B regulatory circuitry impairs B-cell lymphoma survival. Leukemia 33, 2912–2923 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  57. 57.

    Carter, A. J. et al. Target 2035: probing the human proteome. Drug Discov. Today 24, 2111–2115 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  58. 58.

    Mullard, A. A probe for every protein. Nat. Rev. Drug Discov. 18, 733–736 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  59. 59.

    Eng, J. K. et al. A deeper look into Comet – implementation and features. J. Am. Soc. Mass Spectrom. 26, 1865–1874 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Eng, J. K., Jahan, T. A. & Hoopmann, M. R. Comet: an open-source MS/MS sequence database search tool. Proteomics 13, 22–24 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  61. 61.

    Yu, Q. et al. Benchmarking the Orbitrap tribrid eclipse for next generation multiplexed proteomics. Anal. Chem. 92, 6478–6485 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  62. 62.

    Motiwala, H. F., Kuo, Y. H., Stinger, B. L., Palfey, B. A. & Martin, B. R. Tunable heteroaromatic sulfones enhance in-cell cysteine profiling. J. Am. Chem. Soc. 142, 1801–1810 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  63. 63.

    Dephoure, N. & Gygi, S. P. Hyperplexing: a method for higher-order multiplexed quantitative proteomics provides a map of the dynamic response to rapamycin in yeast. Sci. Signal. 5, rs2 (2012).

  64. 64.

    Hacker, S. M. et al. Global profiling of lysine reactivity and ligandability in the human proteome. Nat. Chem. 9, 1181–1190 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Hahm, H. S. et al. Global targeting of functional tyrosines using sulfur-triazole exchange chemistry. Nat. Chem. Biol. 16, 150–159 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  66. 66.

    Navarrete-Perea, J., Yu, Q., Gygi, S. P. & Paulo, J. A. Streamlined Tandem Mass Tag (SL-TMT) protocol: an efficient strategy for quantitative (phospho)proteome profiling using tandem mass tag-synchronous precursor selection-MS3. J. Proteome Res. 17, 2226–2236 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    McAlister, G. C. et al. Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal. Chem. 84, 7469–7478 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Beausoleil, S. A., Villén, J., Gerber, S. A., Rush, J. & Gygi, S. P. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 24, 1285–1292 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  69. 69.

    Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  70. 70.

    Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Komsta, L. & Novomestky, F. moments: moments, cumulants, skewness, kurtosis and related tests. CRAN https://cran.r-project.org/web/packages/moments/moments.pdf (2015).

  72. 72.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  73. 73.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

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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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Authors

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.

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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 (2021). https://doi.org/10.1038/s41587-020-00778-3

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