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:

Monitoring Fe–S cluster occupancy across the E. coli proteome using chemoproteomics

Nature Chemical Biology volume 19, pages 356–366 (2023)Cite this article

Subjects

Abstract

Iron–sulfur (Fe–S) clusters are ubiquitous metallocofactors involved in redox chemistry, radical generation and gene regulation. Common methods to monitor Fe–S clusters include spectroscopic analysis of purified proteins and autoradiographic visualization of radiolabeled iron distribution in proteomes. Here, we report a chemoproteomic strategy that monitors changes in the reactivity of Fe–S cysteine ligands to inform on Fe–S cluster occupancy. We highlight the utility of this platform in Escherichia coli by (1) demonstrating global disruptions in Fe–S incorporation in cells cultured under iron-depleted conditions, (2) determining Fe–S client proteins reliant on five scaffold, carrier and chaperone proteins within the Isc Fe–S biogenesis pathway and (3) identifying two previously unannotated Fe–S proteins, TrhP and DppD. In summary, the chemoproteomic strategy described herein is a powerful tool that reports on Fe–S cluster incorporation directly within a native proteome, enabling the interrogation of Fe–S biogenesis pathways and the identification of previously uncharacterized Fe–S proteins.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: A two-dimensional chemoproteomic platform to monitor Fe–S cluster binding.
Fig. 2: Monitoring reactivity changes for Fe–S-ligating cysteines.
Fig. 3: Interrogating the role of scaffold and carrier proteins within the Isc Fe–S biogenesis pathway.
Fig. 4: Interrogating the role of the chaperone/cochaperone proteins HscA and HscB within the Isc Fe–S biogenesis pathway.
Fig. 5: Mapping coverage of the E. coli Fe–S proteome.
Fig. 6: Predicting Fe–S binding sites from changes in cysteine reactivity.

Similar content being viewed by others

Data availability

MS proteomics data for this study have been deposited in ProteomeXchange via the PRIDE partner repository with the dataset identifier PXD026488. Databases used for MS/MS searches against the E. coli proteome were obtained from the UniProtKB at www.uniprot.org. Source data are provided with this paper.

References

  1. Beinert, H., Holm, R. H. & Münck, E. Iron–sulfur clusters: nature’s modular, multipurpose structures. Science 277, 653–659 (1997).

    Article  CAS  PubMed  Google Scholar 

  2. Hirst, J. Mitochondrial complex I. Annu. Rev. Biochem. 82, 551–575 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Landgraf, B. J., McCarthy, E. L. & Booker, S. J. Radical S-adenosylmethionine enzymes in human health and disease. Annu. Rev. Biochem. 85, 485–514 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Vey, J. L. & Drennan, C. L. Structural insights into radical generation by the radical SAM superfamily. Chem. Rev. 111, 2487–2506 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Boal, A. K. et al. Redox signaling between DNA repair proteins for efficient lesion detection. Proc. Natl Acad. Sci. USA 106, 15237–15242 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Crack, J. C., Green, J., Thomson, A. J. & Le Brun, N. E. Iron–sulfur cluster sensor-regulators. Curr. Opin. Chem. Biol. 16, 35–44 (2012).

    Article  CAS  PubMed  Google Scholar 

  7. Beinert, H., Kennedy, M. C. & Stout, C. D. Aconitase as iron−sulfur protein, enzyme, and iron-regulatory protein. Chem. Rev. 96, 2335–2374 (1996).

    Article  CAS  PubMed  Google Scholar 

  8. Flint, D. H. & Allen, R. M. Iron−sulfur proteins with non-redox functions. Chem. Rev. 96, 2315–2334 (1996).

    Article  CAS  PubMed  Google Scholar 

  9. Rouault, T. A. Iron–sulfur proteins hiding in plain sight. Nat. Chem. Biol. 11, 442–445 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Estellon, J., De Choudens, S. O., Smadja, M., Fontecave, M. & Vandenbrouck, Y. An integrative computational model for large-scale identification of metalloproteins in microbial genomes: a focus on iron–sulfur cluster proteins. Metallomics 6, 1913–1930 (2014).

    Article  CAS  PubMed  Google Scholar 

  11. Sofia, H. J., Chen, G., Hetzler, B. G., Reyes-Spindola, J. F. & Miller, N. E. Radical SAM, a novel protein superfamily linking unresolved steps in familiar biosynthetic pathways with radical mechanisms: functional characterization using new analysis and information visualization methods. Nucleic Acids Res. 29, 1097–1106 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Frey, P. A., Hegeman, A. D. & Ruzicka, F. J. The radical SAM superfamily. Crit. Rev. Biochem. Mol. Biol. 43, 63–88 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. Zhang, Y. et al. Diphthamide biosynthesis requires an organic radical generated by an iron–sulphur enzyme. Nature 465, 891–896 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Braymer, J. J., Freibert, S. A., Rakwalska-Bange, M. & Lill, R. Mechanistic concepts of iron–sulfur protein biogenesis in biology. Biochim. Biophys. Acta Mol. Cell. Res. 1868, 118863–118690 (2020).

    Article  PubMed  Google Scholar 

  15. Rouault, T. A. & Maio, N. Biogenesis and functions of mammalian iron–sulfur proteins in the regulation of iron homeostasis and pivotal metabolic pathways. J. Biol. Chem. 292, 12744–12753 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yan, L. J., Levine, R. L. & Sohal, R. S. Oxidative damage during aging targets mitochondrial aconitase. Proc. Natl Acad. Sci. USA 94, 11168–11172 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Broach, R. B. & Jarrett, J. T. Role of the [2Fe–2S]2+ cluster in biotin synthase: mutagenesis of the atypical metal ligand artginine. Biochemistry 45, 14166–14174 (2006).

    Article  CAS  PubMed  Google Scholar 

  18. Rouault, T. A. et al. An iron-sulfur cluster plays a novel regulatory role in the iron-responsive element binding protein. Biometals 5, 131–140 (1992).

    Article  CAS  PubMed  Google Scholar 

  19. Pierik, A. J., Netz, D. J. & Lill, R. Analysis of iron–sulfur protein maturation in eukaryotes. Nat. Protoc. 4, 753–766 (2009).

    Article  CAS  PubMed  Google Scholar 

  20. Stehling, O., Paul, V. D., Bergmann, J., Basu, S. & Lill, R. Biochemical analyses of human iron–sulfur protein biogenesis and of related diseases. Methods Enzymol. 599, 227–263 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Webert, H. et al. Functional reconstitution of mitochondrial Fe/S cluster synthesis on Isu1 reveals the involvement of ferredoxin. Nat. Commun. 5, 5013 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. Fox, N. G., Chakrabarti, M., McCormick, S. P., Lindahl, P. A. & Barondeau, D. P. The human iron–sulfur assembly complex catalyzes the synthesis of [2Fe–2S] clusters on ISCU2 that can be transferred to acceptor molecules. Biochemistry 54, 3871–3879 (2015).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 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).

    Article  CAS  PubMed  Google Scholar 

  25. 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 

  26. Deng, X. et al. Proteome-wide quantification and characterization of oxidation-sensitive cysteines in pathogenic bacteria. Cell Host Microbe 13, 358–370 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Pace, N. J. & Weerapana, E. A competitive chemical-proteomic platform to identify zinc-binding cysteines. ACS Chem. Biol. 9, 258–265 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Seo, S. W. et al. Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli. Nat. Commun. 5, 4910 (2014).

    Article  CAS  PubMed  Google Scholar 

  29. 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  PubMed Central  Google Scholar 

  30. Boersema, P. J., Raijmakers, R., Lemeer, S., Mohammed, S. & Heck, A. J. Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat. Protoc. 4, 484–494 (2009).

    Article  CAS  PubMed  Google Scholar 

  31. Abo, M., Li, C. & Weerapana, E. Isotopically-labeled iodoacetamide-alkyne probes for quantitative cysteine-reactivity profiling. Mol. Pharm. 15, 743–749 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Forouhar, F. et al. Two Fe–S clusters catalyze sulfur insertion by radical-SAM methylthiotransferases. Nat. Chem. Biol. 9, 333–338 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Becker, A. et al. Iron center, substrate recognition and mechanism of peptide deformylase. Nat. Struct. Biol. 5, 1053–1058 (1998).

    Article  CAS  PubMed  Google Scholar 

  34. Ruzheinikov, S. N. et al. The 1.2 Å structure of a novel quorum-sensing protein, Bacillus subtilis LuxS. J. Mol. Biol. 313, 111–122 (2001).

    Article  CAS  PubMed  Google Scholar 

  35. Yeo, W. S., Lee, J. H., Lee, K. C. & Roe, J. H. IscR acts as an activator in response to oxidative stress for the suf operon encoding Fe–S assembly proteins. Mol. Microbiol. 61, 206–218 (2006).

    Article  CAS  PubMed  Google Scholar 

  36. Burschel, S. et al. Iron–sulfur cluster carrier proteins involved in the assembly of Escherichia coli NADH: ubiquinone oxidoreductase (complex I). Mol. Microbiol. 111, 31–45 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Maio, N. & Rouault, T. A. Iron–sulfur cluster biogenesis in mammalian cells: new insights into the molecular mechanisms of cluster delivery. Biochim. Biophys. Acta 1853, 1493–1512 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. Puglisi, R. & Pastore, A. The role of chaperones in iron–sulfur cluster biogenesis. FEBS Lett. 592, 4011–4019 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Silberg, J. J. & Vickery, L. E. Kinetic characterization of the ATPase cycle of the molecular chaperone Hsc66 from Escherichia coli. J. Biol. Chem. 275, 7779–7786 (2000).

    Article  CAS  PubMed  Google Scholar 

  40. Silberg, J. J., Tapley, T. L., Hoff, K. G. & Vickery, L. E. Regulation of the HscA ATPase reaction cycle by the co-chaperone HscB and the iron–sulfur cluster assembly protein IscU. J. Biol. Chem. 279, 53924–53931 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Bonomi, F., Iametti, S., Morleo, A., Ta, D. & Vickery, L. E. Studies on the mechanism of catalysis of iron–sulfur cluster transfer from IscU [2Fe2S] by HscA/HscB chaperones. Biochemistry 47, 12795–12801 (2008).

    Article  CAS  PubMed  Google Scholar 

  42. Iametti, S., Barbiroli, A. & Bonomi, F. Functional implications of the interaction between HscB and IscU in the biosynthesis of FeS clusters. J. Biol. Inorg. Chem. 20, 1039–1048 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Maio, N. et al. Cochaperone binding to LYR motifs confers specificity of iron sulfur cluster delivery. Cell Metab. 19, 445–457 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Maio, N., Kim, K. S., Singh, A. & Rouault, T. A. A single adaptable cochaperone-scaffold complex delivers nascent iron-sulfur clusters to mammalian respiratory chain complexes I–III. Cell Metab. 25, 945–953 (2017).

    Article  CAS  PubMed  Google Scholar 

  45. Hinchliffe, P. & Sazanov, L. A. Organization of iron–sulfur clusters in respiratory complex I. Science 309, 771–774 (2005).

    Article  CAS  PubMed  Google Scholar 

  46. Unden, G. & Bongaerts, J. Alternative respiratory pathways of Escherichia coli: energetics and transcriptional regulation in response to electron acceptors. Biochim. Biophys. Acta 1320, 217–234 (2017).

    Article  Google Scholar 

  47. Sakai, Y., Kimura, S. & Suzuki, T. Dual pathways of tRNA hydroxylation ensure efficient translation by expanding decoding capability. Nat. Commun. 10, 2858 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Kimura, S., Sakai, Y., Ishiguro, K. & Suzuki, T. Biogenesis and iron-dependency of ribosomal RNA hydroxylation. Nucleic Acids Res. 45, 12974–12986 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Pelosi, L. et al. Ubiquinone biosynthesis over the entire O2 range: characterization of a conserved O2-independent pathway. mBio 10, e01319-19 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Locher, K. P. Mechanistic diversity in ATP-binding cassette (ABC) transporters. Nat. Struct. Mol. Biol. 23, 487–493 (2016).

    Article  CAS  PubMed  Google Scholar 

  51. Baba, T. et al. Construction of Escherichia coli K‐12 in‐frame, single‐gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Neidhardt, F. C., Bloch, P. L. & Smith, D. F. Culture medium for enterobacteria. J. Bacteriol. 119, 736–747 (1974).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).

    Article  CAS  PubMed  Google Scholar 

  54. Frazzon, J. & Dean, D. R. Formation of iron–sulfur clusters in bacteria: an emerging field in bioinorganic chemistry. Curr. Opin. Chem. Biol. 7, 166–173 (2003).

    Article  CAS  PubMed  Google Scholar 

  55. Edwards, A. & Haas, W. Multiplexed quantitative proteomics for high-throughput comprehensive proteome comparisons of human cell lines. Methods Mol. Biol. 1394, 1–13 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994).

    Article  CAS  PubMed  Google Scholar 

  57. Tabb, D. L., McDonald, W. H. & Yates, J. R. DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J. Proteome Res. 1, 21–26 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Käll, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923–925 (2007).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  60. Bak, D. W., Pizzagalli, M. D. & Weerapana, E. Identifying functional cysteine residues in the mitochondria. ACS Chem. Biol. 12, 947–957 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all members of the E.W. laboratory for discussions and feedback. This work was supported by NIH grant R35GM134964 to E.W.

Author information

Authors and Affiliations

  1. Department of Chemistry, Boston College, Chestnut Hill, MA, USA

    Daniel W. Bak & Eranthie Weerapana

Authors
  1. Daniel W. Bak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eranthie Weerapana
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.W.B. and E.W. designed the research. D.W.B. performed the research and proteomic analyses. D.W.B. and E.W. analyzed and interpreted the data. D.W.B. and E.W. wrote the paper.

Corresponding authors

Correspondence to Daniel W. Bak or Eranthie Weerapana.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Chemical Biology thanks the anonymous reviewers for their contribution to the peer review of this work.

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 A functional catalogue of the E. coli Fe–S proteome.

The 144 members of the E. coli Fe-S proteome categorized by cluster function (see Supplementary Dataset 1). General Fe-S proteins/complexes (blue) are represented as cartoon diagrams displaying the approximate orientation of all (metallo)cofactors, with a list of individual Fe-S family members presented in the associated green boxes. Abbreviations: XDH, xanthine dehydrogenase; DMSO, dimethyl sulfoxide reductase; SDH, succinate dehydrogenase; FRD, fumarate reductase; GltS, glutamate synthase; NiFe, dinuclear nickel-iron center; SAM, S-adenosylmethionine; Moco, molybdenum cofactor.

Extended Data Fig. 2 Cysteine labeling and protein abundance changes in the E. coli Fe-S proteome upon iron-depletion.

a, b, Intracellular (a) iron and (b) zinc concentration determined by ICP-OES for E. coli grown under iron-replete (control) (gray) or iron-depleted (green) conditions. Significance is calculated as *** p < 0.005 (p = 2.2E-05), paired t-test (two-tailed) from n = 3 biological replicates. Error bars represent the standard error of the mean. c, Reductive dimethylation (ReDiMe) proteomic platform employed to measure protein abundance changes upon growth of E. coli under iron-depletion conditions. d, IsoTOP-ABPP proteomic platform employed to measure cysteine labeling changes upon growth of E. coli under iron-depletion conditions. e, Waterfall plot of log2 L/H ratio (RP) changes for all quantified proteins from an iron-depleted E. coli proteome (see Supplementary Dataset 2). Proteins with RP>|0.5| are highlighted in red. f, Gene-ontology analysis of processes enriched in proteins with RP>0.5. Processes involved in iron import and homeostasis are highlighted in red. g, Pie chart analysis of the number of quantified Fe-S proteins with RP<−0.5 (red), RP ~ 0.0 (gray), and RP>0.5 (green).

Source data

Extended Data Fig. 3 Cysteine reactivity under iron-depletion is independent of lysate preparation and IA-alkyne labeling conditions.

a, Two-dimensional proteomic dataset for the E. coli proteome grown under iron-replete (control) conditions (see Supplementary Dataset 3). All quantified cysteine residues are plotted in the main graph (annotated Fe-S cluster cysteine ligands - green circles, non-annotated cysteine residues - light gray small circles). Inset to right: cysteine residues with no protein abundance data (annotated Fe-S cluster cysteine ligands - green circles, non-annotated cysteine residues - light gray small circles). Inset below: proteins with no cysteine reactivity data (annotated Fe-S protein - red circles, non-annotated proteins - light gray small). b,c, Comparison plot of cysteine reactivity ratios (iron-depleted versus iron-replete) for quantified Fe-S ligands (green circles) from lysates IA-alkyne labeled under standard aerobic (x-axis) versus (b) anaerobic or (c) reducing conditions (y-axis) (see Supplementary Dataset 2).

Extended Data Fig. 4 Cysteine reactivity changes for the ligands of the two Fe-S clusters of biotin synthase.

a, Rc values for Fe-S cysteine ligands on the Fe-S protein, BioB. b, Crystal structure of E. coli BioB with clusters and cysteine residues highlighted (Insets: AdoMet radical [4Fe-4S] cluster – green and auxiliary [4Fe-4S] cluster – orange).

Extended Data Fig. 5 suf operon expression in E. coli isc genetic deletion strains.

a, Structural organization of the E. coli isc and suf operons. b, Proposed model of suf operon function and regulation by the IscU client protein and transcription factor, IscR. c, Protein abundance changes for scaffold complex proteins from the suf operon across all experimental growth conditions and deletion strains.

Extended Data Fig. 6 Reactivity profile of cysteine residues involved in Fe-S ligation.

a, Proteomic workflow for measuring the cysteine reactivity of Fe-S cluster ligands in an iron-depleted E. coli proteome labeled with 100 µM IA-Light or 10 µM IA-Heavy. b, Cysteine reactivity curve for all quantified cysteine residues (light gray) from an iron-depleted E. coli proteome (see Supplementary Dataset 9). Fe-S cluster cysteine ligands are highlighted (green circles). c,d Violin plot of L/H ratios for unique groups of (c) functional cysteine residues, including Fe-S ligands (red), active site residues (blue), zinc ligands (purple), disulfides (green), and iron ligands (yellow) and (d) Fe-S ligands from Fe-S client proteins (red) and Fe-S scaffold proteins (yellow). The median R value for each functional group of cysteine residues is displayed as a dashed line, while the average R values (red) and number of unique values (black) are indicated for each group.

Extended Data Fig. 7 Biochemical characterization of the putative Fe-S protein DppD.

a, Organization of the dpp and opp operons. b, the protein domain structure of the nucleotide-binding subunits, DppD/F and OppD/F. Conserved cysteine residues in the C-terminal extension (orange) are shown as red lines. c, AlphaFold structural model of DppD (AF-P0AAG0-F1). The four conserved c-terminal cysteine residues are highlighted. d, Representative SDS-PAGE analysis of purified E. coli DppD (experiment independently repeated twice). e, Visible color of purified E. coli DppD protein. f, ICP-OES analysis of the iron content of purified E. coli DppD from n = 3 experimental replicates. Error bars represent the standard error of the mean. g, UV-visible absorbance spectrum of as-isolated E. coli DppD.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Tables 1 and 2.

Reporting Summary

Supplementary Data

MS-based proteomic data.

Source data

Source Data Fig. 1

UV-visible spectroscopy data.

Source Data Fig. 1

Unprocessed SDS–PAGE gels.

Source Data Fig. 6

ICP-OES, UV-visible spectroscopy and EPR spectroscopy data.

Source Data Fig. 6

Unprocessed SDS–PAGE gels.

Source Data Extended Data Fig./Table 2

ICP-OES data.

Source Data Extended Data Fig. 7

ICP-OES and UV-visible spectroscopy data.

Source Data Extended Data Fig. 7

Unprocessed SDS–PAGE gels.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bak, D.W., Weerapana, E. Monitoring Fe–S cluster occupancy across the E. coli proteome using chemoproteomics. Nat Chem Biol 19, 356–366 (2023). https://doi.org/10.1038/s41589-022-01227-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41589-022-01227-9

This article is cited by

Search

Advanced 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