Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Global profiling of phosphorylation-dependent changes in cysteine reactivity

Abstract

Proteomics has revealed that the ~20,000 human genes engender a far greater number of proteins, or proteoforms, that are diversified in large part by post-translational modifications (PTMs). How such PTMs affect protein structure and function is an active area of research but remains technically challenging to assess on a proteome-wide scale. Here, we describe a chemical proteomic method to quantitatively relate serine/threonine phosphorylation to changes in the reactivity of cysteine residues, a parameter that can affect the potential for cysteines to be post-translationally modified or engaged by covalent drugs. Leveraging the extensive high-stoichiometry phosphorylation occurring in mitotic cells, we discover numerous cysteines that exhibit phosphorylation-dependent changes in reactivity on diverse proteins enriched in cell cycle regulatory pathways. The discovery of bidirectional changes in cysteine reactivity often occurring in proximity to serine/threonine phosphorylation events points to the broad impact of phosphorylation on the chemical reactivity of proteins and the future potential to create small-molecule probes that differentially target proteoforms with PTMs.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Cysteine reactivity profiling of mitotic and asynchronous cells.
Fig. 2: Proteomic mapping of phosphorylation-dependent changes in cysteine reactivity.
Fig. 3: Adapted protocol for interpreting proximal phosphorylation-cysteine interactions.
Fig. 4: Features of proteins with mitotic phosphorylation-dependent changes in cysteine reactivity.

Similar content being viewed by others

Data availability

All mass spectrometry data are available via PRIDE with identifier PXD026730. Source data, in addition to Supplementary Dataset 1, is available for all figure panels. Source data are provided with this paper.

Code availability

TMT-based data output from Integrated Proteomics Pipeline (IP2) and isoTOP data output from CIMAGE was further analyzed with custom scripts, available on Zenodo at https://zenodo.org/badge/latestdoi/419072418.

References

  1. Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Forbes, S. A. et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–11 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Consortium, E. P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  4. The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  5. Spreafico, R. et al. Advances in genomics for drug development. Genes 11, 942 (2020).

  6. Aebersold, R. et al. How many human proteoforms are there? Nat. Chem. Biol. 14, 206–214 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jensen, O. N. Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr. Opin. Chem. Biol. 8, 33–41 (2004).

    Article  PubMed  Google Scholar 

  8. Cai, W. et al. Top-Down proteomics of large proteins up to 223 kDa enabled by serial size exclusion chromatography strategy. Anal. Chem. 89, 5467–5475 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chen, B., Brown, K. A., Lin, Z. & Ge, Y. Top-down proteomics: ready for prime time? Anal. Chem. 90, 110–127 (2018).

    Article  CAS  PubMed  Google Scholar 

  10. Zhang, Y., Fonslow, B. R., Shan, B., Baek, M. C. & Yates, J. R. 3rd Protein analysis by shotgun/bottom-up proteomics. Chem. Rev. 113, 2343–94 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Low, T. Y. et al. Widening the bottleneck of phosphoproteomics: evolving strategies for phosphopeptide enrichment. Mass Spectrom. Rev. 40, 309–333 (2020).

  12. Riley, N. M., Bertozzi, C. R. & Pitteri, S. J. A pragmatic guide to enrichment strategies for mass spectrometry-based glycoproteomics. Mol. Cell Proteom. 20, 100029 (2020).

    Article  Google Scholar 

  13. Martin, B. R. & Cravatt, B. F. Large-scale profiling of protein palmitoylation in mammalian cells. Nat. Methods 6, 135–8 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Huang, J. X. et al. High throughput discovery of functional protein modificationS by Hotspot Thermal Profiling. Nat. Methods 16, 894–901 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Batty, P. & Gerlich, D. W. Mitotic chromosome mechanics: how cells segregate their genome. Trends Cell Biol. 29, 717–726 (2019).

    Article  PubMed  Google Scholar 

  16. Mahdessian, D. et al. Spatiotemporal dissection of the cell cycle with single-cell proteogenomics. Nature 590, 649–654 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Levine, M. S. & Holland, A. J. The impact of mitotic errors on cell proliferation and tumorigenesis. Genes Dev. 32, 620–638 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Collins, K., Jacks, T. & Pavletich, N. P. The cell cycle and cancer. Proc. Natl Acad. Sci. USA 94, 2776–8 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–74 (2011).

    Article  CAS  PubMed  Google Scholar 

  20. Cuijpers, S. A. G. & Vertegaal, A. C. O. Guiding mitotic progression by crosstalk between post-translational modifications. Trends Biochem. Sci. 43, 251–268 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 8, 1583–94 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. Olsen, J. V. et al. Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal 3, ra3 (2010).

    Article  PubMed  Google Scholar 

  23. Kettenbach, A. N. et al. Quantitative phosphoproteomics identifies substrates and functional modules of Aurora and Polo-like kinase activities in mitotic cells. Sci. Signal 4, rs5 (2011).

    Article  CAS  PubMed  Google Scholar 

  24. Dephoure, N. et al. A quantitative atlas of mitotic phosphorylation. Proc. Natl Acad. Sci. USA 105, 10762–7 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dai, L. et al. Modulation of protein-interaction states through the cell cycle. Cell 173, 1481–1494 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Becher, I. et al. Pervasive protein thermal stability variation during the cell cycle. Cell 173, 1495–1507 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Heusel, M. et al. A global screen for assembly state changes of the mitotic proteome by SEC-SWATH-MS. Cell Syst. 10, 133–155 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wani, R., Nagata, A. & Murray, B. W. Protein redox chemistry: post-translational cysteine modifications that regulate signal transduction and drug pharmacology. Front. Pharm. 5, 224 (2014).

    Article  Google Scholar 

  29. Chung, H. S., Wang, S. B., Venkatraman, V., Murray, C. I. & Van Eyk, J. E. Cysteine oxidative posttranslational modifications: emerging regulation in the cardiovascular system. Circ. Res. 112, 382–92 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Backus, K. M. Applications of reactive cysteine profiling. Curr. Top. Microbiol. Immunol. 420, 375–417 (2019).

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Weerapana, E. et al. Quantitative reactivity profiling predicts functional cysteines in proteomes. Nature 468, 790–795 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Pan, J. & Carroll, K. S. Chemical biology approaches to study protein cysteine sulfenylation. Biopolymers 101, 165–72 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Couvertier, S. M., Zhou, Y. & Weerapana, E. Chemical-proteomic strategies to investigate cysteine posttranslational modifications. Biochim. Biophys. Acta 1844, 2315–30 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Gould, N. S. et al. Site-specific proteomic mapping identifies selectively modified regulatory cysteine residues in functionally distinct protein networks. Chem. Biol. 22, 965–75 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  40. Bar-Peled, L. et al. Chemical proteomics identifies druggable vulnerabilities in a genetically defined cancer. Cell 171, 696–709 e23 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wang, C., Weerapana, E., Blewett, M. M. & Cravatt, B. F. A chemoproteomic platform to quantitatively map targets of lipid-derived electrophiles. Nat. Methods 11, 79–85 (2014).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Patterson, J. C. et al. ROS and oxidative stress are elevated in mitosis during asynchronous cell cycle progression and are exacerbated by mitotic arrest. Cell Syst. 8, 163–167(2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. van der Reest, J., Lilla, S., Zheng, L., Zanivan, S. & Gottlieb, E. Proteome-wide analysis of cysteine oxidation reveals metabolic sensitivity to redox stress. Nat. Commun. 9, 1581 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z. & Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 47, W199–W205 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Supek, F., Bosnjak, M., Skunca, N. & Smuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ostermann, N. et al. Insights into the phosphoryltransfer mechanism of human thymidylate kinase gained from crystal structures of enzyme complexes along the reaction coordinate. Structure 8, 629–42 (2000).

    Article  CAS  PubMed  Google Scholar 

  48. Meszaros, B., Erdos, G. & Dosztanyi, Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res. 46, W329–W337 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Matsumoto, T. et al. Crystal structures of MKK4 kinase domain reveal that substrate peptide binds to an allosteric site and induces an auto-inhibition state. Biochem. Biophys. Res. Commun. 400, 369–373 (2010).

    Article  CAS  PubMed  Google Scholar 

  50. Zhao, H. et al. AMPK-mediated activation of MCU stimulates mitochondrial Ca2+ entry to promote mitotic progression. Nat. Cell Biol. 21, 476–486 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Marcussen, M. & Larsen, P. J. Cell cycle-dependent regulation of cellular ATP concentration, and depolymerization of the interphase microtubular network induced by elevated cellular ATP concentration in whole fibroblasts. Cell Motil. Cytoskeleton 35, 94–99 (1996).

    Article  CAS  PubMed  Google Scholar 

  52. Becher, I. et al. Affinity profiling of the cellular kinome for the nucleotide cofactors ATP, ADP, and GTP. ACS Chem. Biol. 8, 599–607 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Tibbles, L. A. et al. MLK-3 activates the SAPK/JNK and p38/RK pathways via SEK1 and MKK3/6. EMBO J. 15, 7026–7035 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Posewitz, M. C. & Tempst, P. Immobilized gallium(III) affinity chromatography of phosphopeptides. Anal. Chem. 71, 2883–2892 (1999).

    Article  CAS  PubMed  Google Scholar 

  55. Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Iakoucheva, L. M. et al. The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res. 32, 1037–1049 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zhu, H. et al. Crystal structures of the tetratricopeptide repeat domains of kinesin light chains: insight into cargo recognition mechanisms. PLoS ONE 7, e33943 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Hirokawa, N. & Noda, Y. Intracellular transport and kinesin superfamily proteins, KIFs: structure, function, and dynamics. Physiol. Rev. 88, 1089–1118 (2008).

    Article  CAS  PubMed  Google Scholar 

  59. Vagnoni, A., Rodriguez, L., Manser, C., De Vos, K. J. & Miller, C. C. Phosphorylation of kinesin light chain 1 at serine 460 modulates binding and trafficking of calsyntenin-1. J. Cell Sci. 124, 1032–1042 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Batut, J., Howell, M. & Hill, C. S. Kinesin-mediated transport of Smad2 is required for signaling in response to TGF-beta ligands. Dev. Cell 12, 261–274 (2007).

    Article  CAS  PubMed  Google Scholar 

  61. Savaryn, J. P., Catherman, A. D., Thomas, P. M., Abecassis, M. M. & Kelleher, N. L. The emergence of top-down proteomics in clinical research. Genome Med. 5, 53 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zheng, Y. et al. Unabridged analysis of human histone H3 by differential top-down mass spectrometry reveals hypermethylated proteoforms from MMSET/NSD2 overexpression. Mol. Cell Proteom. 15, 776–790 (2016).

    Article  CAS  Google Scholar 

  63. Ntai, I. et al. Precise characterization of KRAS4b proteoforms in human colorectal cells and tumors reveals mutation/modification cross-talk. Proc. Natl Acad. Sci. USA 115, 4140–4145 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Floyd, B. M., Drew, K. & Marcotte, E. M. Systematic identification of protein phosphorylation-mediated interactions. J. Proteome Res. 20, 1359–1370 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Abo, M. & Weerapana, E. A caged electrophilic probe for global analysis of cysteine reactivity in living cells. J. Am. Chem. Soc. 137, 7087–90 (2015).

    Article  CAS  PubMed  Google Scholar 

  66. Telles, E., Gurjar, M., Ganti, K., Gupta, D. & Dalal, S. N. Filamin A stimulates cdc25C function and promotes entry into mitosis. Cell Cycle 10, 776–82 (2011).

    Article  CAS  PubMed  Google Scholar 

  67. Szeto, S. G. Y., Williams, E. C., Rudner, A. D. & Lee, J. M. Phosphorylation of filamin A by Cdk1 regulates filamin A localization and daughter cell separation. Exp. Cell Res. 330, 248–266 (2015).

    Article  CAS  PubMed  Google Scholar 

  68. van der Vaart, B. et al. SLAIN2 links microtubule plus end-tracking proteins and controls microtubule growth in interphase. J. Cell Biol. 193, 1083–99 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Wang, W. et al. Discovery of an allosteric, inactive conformation-selective inhibitor of full-length HPK1 utilizing a kinase cascade assay. Biochemistry 60, 3114–3124 (2021).

  70. Paulsen, C. E. et al. Peroxide-dependent sulfenylation of the EGFR catalytic site enhances kinase activity. Nat. Chem. Biol. 8, 57–64 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Schlicker, A., Domingues, F. S., Rahnenfuhrer, J. & Lengauer, T. A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinformatics 7, 302 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank V. Vartabedian from the Teijaro lab for technical assistance and G. Simon (Vividion) and S. Niessen and M. Hayward (Pfizer) for valuable feedback throughout this project. This work was supported by the National Institutes of Health (NIH) (CA231991 to B. F. C), NIH-NCI (CA239556 to E. K. K.), and Vividion Therapeutics.

Author information

Authors and Affiliations

Authors

Contributions

B. F. C. and E. K. K. conceived of the project, analyzed data, and wrote the manuscript. E. K. K. and M. M. D. developed mass spectrometry methods. E. K. K. and Y. Z. performed experiments.

Corresponding authors

Correspondence to Esther K. Kemper or Benjamin F. Cravatt.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Dustin Maly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Editor recognition statement Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Cysteine reactivity profiling of mitotic and asynchronous cells.

a, Timeline for mitotic HeLa cell proteome generation. b, Cysteine reactivity values for GAPDH and PARK7 (Mitosis/Asynch) cell proteomes. Horizontal black line for each cysteine marks median value, boxes mark the upper and lower quartiles, and whiskers mark 1.5x interquartile range for n = 9 independent experiments (circles). Dotted lines designate boundaries for ≥ twofold changes. c, TMT-ABPP workflow for measuring protein expression (top, blue) and cysteine reactivity (bottom, gray) in the mitotic and asynchronous HeLa cell proteome. d, Venn diagram showing overlap (light blue) in proteins quantified by TMT-ABPP (gray) and unenriched proteomics (dark blue). For inclusion, proteins had at least one quantified cysteine in at least two replicate experiments of TMT-ABPP and/or two unique quantified peptides quantified from at least one replicate of unenriched proteomics. e, GO cellular analysis of proteins with reactivity-based cysteine changes in mitotic vs asynchronous cell proteomes45,46. Proteins with reactivity-based cysteine changes correspond to those defined in Fig. 1d. f, Box plot showing DTYMK cysteine reactivity values (Mitosis/Asynch). Horizontal black line for each cysteine marks median value, boxes mark the upper and lower quartiles, and whiskers mark 1.5x interquartile range for n = 5 (or more) independent experiments (circles). Dotted lines designate boundaries for ≥ twofold changes. g, Cysteine reactivity values for all quantified DTYMK cysteines following gel filtration of the mitotic cell proteome. Data represent average values + /- standard deviation for n = 3 independent experiments (circles). h, DTYMK C117 reactivity following gel filtration of asynchronous (gray) vs mitotic (blue) cell proteomes. Data represent average values + /- standard deviation for n = 3 (or more) independent experiments (circles). i, X-ray crystal structure of DTYMK in complex with AMP and TMP with C163 and C117 highlighted in yellow (PDB: 1E2D)47. j, Protein expression values for DTWD1 (not detected), NOL8, and RRP15 (Mitosis/Asynch). Horizontal black line for each cysteine marks median value, boxes mark the upper and lower quartiles, and whiskers mark 1.5x interquartile range for n = 9 independent experiments (circles). Dotted lines designate boundaries for ≥ twofold changes.

Source data

Extended Data Fig. 2 A proteomic method to map phosphorylation-dependent changes in cysteine reactivity.

a, Venn diagram of phosphorylated S/T residues quantified by Sharma et al.21 (red) and this study (dark blue) in asynchronous and mitotic cell proteomes. b, Cysteine reactivity ratio values in Native/Denatured mitotic proteome. Light blue and orange data mark cysteine reactivity values that are ≥ twofold higher (boundary marked by dotted lines) in native and denatured cell proteome, respectively. Data are the median value for n = 1 (or more) independent experiments. c, Comparison of cysteine reactivity values from LPP(-)/LPP( + ) (y-axis) and Native/Denatured (x-axis) proteomes. Blue and red data mark cysteine reactivity values that are ≥ twofold higher in LPP(-) and LPP( + ) cell proteomes, respectively. Light blue and orange data mark cysteines that are unchanging in LPP(-)/LPP( + ), but changing twofold in Native/Denatured mitotic proteomes. Dotted lines mark boundaries for cysteines that change ≥ two-fold in reactivity in LPP(-)/LPP( + ) and Native/Denatured. Data are the median value for n = 2 (or more) independent LPP(-)/LPP( + ) experiments and n = 1 (or more) Native/Denatured experiments. d-f, Cysteine reactivity values across Native/Denatured proteome (orange) and LPP(-)/LPP( + ) (green) mitotic proteome for cysteines in d) FLNB, e) NUMA1, and f) BAG3. Horizontal black lines mark median value, boxes mark upper and lower quartiles, and whiskers mark 1.5x interquartile range for n = 2 (or more) independent experiments (circles). Dotted lines designate boundaries for ≥ twofold changes. g, Percentage of cysteines in predicted disordered domains (IUPreD ≥ 0.5)48. h, Cysteine reactivity values for MAP2K4 in gel-filtred mitotic cell proteome. Data represent the average values + /- standard deviation for n = 2 (or more) independent experiments (circles). i, MAP2K4 C246 reactivity in gel-filtered asynchronous (gray) vs mitotic (blue) cell proteomes. Data represent the average values + /- standard deviation for n = 4 (or more) independent experiments (circles). j, Left, MAP2K ATP-binding pocket cysteine reactivity. Non-unique peptides are assigned to both MAP2Ks. Horizontal black lines mark median value, boxes mark upper and lower quartiles, and whiskers mark 1.5x interquartile range for n = 2 (or more) independent experiments (circles). Dotted lines designate boundaries for ≥ two-fold changes. Right, sequence alignment of MAP2K proteins centered on MAP2K4 ATP-binding pocket C246.

Source data

Extended Data Fig. 3 Adapted protocol for interpreting proximal phosphorylation-cysteine interactions.

a, Left, cysteine reactivity values for indicated comparison groups for quantified cysteines from ECD3 (C137, top) and GTF2I (C215, bottom). Horizontal black line for each cysteine marks median value, boxes mark upper and lower quartiles, and whiskers mark 1.5x interquartile range for n = 5 (or more) independent experiments (circles). Dotted lines designate boundaries for ≥ two-fold changes. Right, tryptic peptides containing EDC3 C137 (asterisks, red, bold; top) and GTF2I C215 (asterisks, red, bold; bottom) and high occupancy phosphorylation sites (black, bold)21 b, Left, bar graph showing phosphopeptide enrichment of EDC3 p-S131 (top) and GTF2I p-S210 (bottom). Data were normalized to mitotic proteome without LPP treatment (Mitosis LPP(-)) and represent the median values ± standard deviation for n = 3 independent experiments (circles). Right, tryptic peptides containing phosphorylated (p-, purple, bold) EDC p-S131 (asterisks, purple, bold; top) and GTF2I p-S210 (asterisks, purple, bold; bottom) and cysteines from Extended Data Fig. 3a marked (red, bold). c, Immunoblot analysis of MAP2K1 with antibody #9146 in mitosis. Data are from a single experiment representative of two independent experiments. d, FLNA cysteine reactivity values across the indicated comparison groups. Horizontal black line for each cysteine marks median value, boxes mark the upper and lower quartiles, and whiskers mark 1.5x interquartile range for n = 5 (or more) independent experiments (circles). Dotted lines designate boundaries for ≥ two-fold changes. e, Left, SLAIN2 C152 reactivity values across the indicated comparison groups. Horizontal black line for each cysteine marks median value, boxes mark the upper and lower quartiles, and whiskers mark 1.5x interquartile range for n = 3 (or more) independent experiments (circles). Dotted lines designate boundaries for ≥ two-fold changes. Right, tryptic peptide containing SLAIN2 C152 (asterisks, blue, bold) and a potential S/T-P phosphorylation site (black, bold). f, Left, bar graph showing phosphopeptide enrichment of SLAIN2 p-S147. Data were normalized to mitotic proteome without LPP treatment (Mitosis LPP(-)) and are from n = 1 experiment. Right, tryptic peptide containing phosphorylated (p-, purple, bold) SLAIN2 S147 (asterisks, purple, bold) with cysteine from Extended Data Fig. 3e marked (blue, bold).

Source data

Extended Data Fig. 4 Features of proteins with mitotic phosphorylation-dependent changes in cysteine reactivity.

a, Proteins with authentic (left) and artifactual (middle) phosphorylation-dependent cysteine reactivity changes are enriched for high stoichiometry mitotic phosphorylation sites21 (blue) compared to all quantified proteins (right). Proteins lacking sufficient data for phosphorylation stoichiometry calculation or exhibiting only low stoichiometry (< 50% occupancy) sites21 were labeled as ‘Low or unquantified stoichiometry’ (orange). b, Proteins with phosphorylation-dependent cysteine reactivity changes (left) are enriched for LPP-sensitive mitotic phosphorylation sites (purple) compared to all quantified proteins (right). Proteins with only artifactual cysteine reactivity changes were removed from analysis. Proteins with LPP-insensitive or no quantified phosphorylation sites were labeled as “Unchanging or unquantified phosphosites” (gray). c, Members of the anaphase-promoting complex (APC/C) of the KEGG cell cycle pathway (HSA04110).55 Proteins are as described in Fig. 4b. d, Fraction of cysteines showing phosphorylation-dependent reactivity changes within the specified amino acid distances from an S/T-P site. Artifactual phosphorylation-dependent cysteine reactivity changes were omitted from analysis. e, Fraction of cysteines showing authentic (left) versus artifactual (right) phosphorylation-dependent reactivity changes within the specified amino acid distances from an S/T-P site. Authentic and artifactual changes were determined as described in Fig. 3f. f, Sequence alignment of the KLC1 and KLC2 proteins centered on C456 and C441, respectively (asterisks, red, bold). Known (KLC1) and predicted (KLC2) S-P phosphorylation motifs are marked (black, bold). g, KLC1 cysteine reactivity values across the indicated comparison groups. Horizontal black lines mark median value, boxes mark upper and lower quartiles, and whiskers mark 1.5x interquartile range for n = 2 (or more) independent experiments (circles). Dotted lines designate boundaries for ≥ two-fold changes. h, FXR2 C270 reactivity values for indicated comparison groups. Horizontal black lines mark median value, boxes mark upper and lower quartiles, and whiskers mark 1.5x interquartile range for n = 5 (or more) independent experiments (circles). Dotted lines designate boundaries for ≥ two-fold changes. i, Left, phosphopeptide enrichment of FXR2 p-S450. Data were normalized to Mitosis LPP(-) and represent the median values + /- standard deviation for n = 2 independent experiments (circles). Right, tryptic peptides containing phosphorylated (p-, purple, bold) FXR2 p-S450 (asterisks, purple, bold).

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical source data

Source Data Fig. 2

Statistic source data and unprocessed western blots

Source Data Fig. 3

Statistic source data and unprocessed western blots

Source Data Fig. 4

Statistical source data

Source Data Extended Data Fig. 1

Statistical source data

Source Data Extended Data Fig. 2

Statistical source data

Source Data Extended Data Fig. 3

Statistical source data and unprocessed western blots

Source Data Extended Data Fig. 4

Statistical source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kemper, E.K., Zhang, Y., Dix, M.M. et al. Global profiling of phosphorylation-dependent changes in cysteine reactivity. Nat Methods 19, 341–352 (2022). https://doi.org/10.1038/s41592-022-01398-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-022-01398-2

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research