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.

  • Protocol
  • Published:

Systems structural biology measurements by in vivo cross-linking with mass spectrometry

Abstract

This protocol describes a workflow for utilizing large-scale cross-linking with mass spectrometry (XL-MS) to make systems-level structural biology measurements in complex biological samples, including cells, isolated organelles, and tissue samples. XL-MS is a structural biology technique that provides information on the molecular structure of proteins and protein complexes using chemical probes that report the proximity of probe-reactive amino acids within proteins, typically lysine residues. Information gained through XL-MS studies is often complementary to more traditional methods, such as X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy. The use of MS-cleavable cross-linkers, including protein interaction reporter (PIR) technologies, enables XL-MS studies on protein structures and interactions in extremely complex biological samples, including intact living cells. PIR cross-linkers are designed to contain chemical bonds at specific locations within the cross-linker molecule that can be selectively cleaved by collision-induced dissociation or UV light. When broken, these bonds release the intact peptides that were cross-linked, as well as a reporter ion. Conservation of mass dictates that the sum of the two released peptide masses and the reporter mass equals the measured precursor mass. This relationship is used to identify cross-linked peptide pairs. Release of the individual peptides permits accurate measurement of their masses and independent amino acid sequence determination by tandem MS, allowing the use of standard proteomics search engines such as Comet for peptide sequence assignment, greatly simplifying data analysis of cross-linked peptide pairs. Search results are processed with XLinkProphet for validation and can be uploaded into XlinkDB for interaction network and structural analysis.

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: Synthesis and molecular features of PIR cross-linker.
Fig. 2: Experimental overview of in vivo XL-MS.
Fig. 3: Data analysis workflow.
Fig. 4: LC–ESI–MS analysis of the BDP–NHP cross-linker.
Fig. 5: Cross-links mapped onto BSA structure.
Fig. 6: Comparison of structural models for the mitochondrial trifunctional enzyme complex generated by XL-MS guided docking and cryo-EM.

Similar content being viewed by others

Data availability

Data are freely available online. Raw data from Chavez, J. D. et al. Cell Syst. 6, 136–141 (2018) https://doi.org/10.1016/j.cels.2017.10.017 (used for Fig. 6) are available in the PRIDE Archive (http://www.ebi.ac.uk/pride/archive/) with accession no. PXD007673. Cross-linking data are available in XLinkDB (http://xlinkdb.gs.washington.edu/) under network name ChavezCellSystems2017 BruceLab.

Code availability

All necessary software is freely available for the foreseeable future online at the respective sources: XLinkProphet (https://github.com/brucelab/xlinkprophet), mango (https://github.com/jpm369/mango), Comet (http://comet-ms.sourceforge.net/), ReAdW (https://sourceforge.net/projects/sashimi/files/ReAdW%20%28Xcalibur%20converter%29/), and perl (https://www.perl.org/get.html). In addition, the versions of these software used in this protocol are available in the Supplementary Software file.

References

  1. Holding, A. N. XL-MS: protein cross-linking coupled with mass spectrometry. Methods 89, 54–63 (2015).

    Article  CAS  Google Scholar 

  2. Leitner, A. et al. Crosslinking and mass spectrometry: an integrated technology to understand the structure and function of molecular machines. Trends Biochem. Sci. 41, 20–32 (2016).

    Article  CAS  Google Scholar 

  3. Paramelle, D. et al. Chemical cross-linkers for protein structure studies by mass spectrometry. Proteomics 13, 438–456 (2013).

    Article  CAS  Google Scholar 

  4. Sinz, A. Divide and conquer: cleavable cross-linkers to study protein conformation and protein–protein interactions. Anal. Bioanal. Chem. 409, 33–44 (2017).

    Article  CAS  Google Scholar 

  5. Rappsilber, J. The beginning of a beautiful friendship: cross-linking/mass spectrometry and modelling of proteins and multi-protein complexes. J. Struct. Biol. 173, 530–540 (2011).

    Article  CAS  Google Scholar 

  6. Anderson, G. A. et al. Informatics strategies for large-scale novel cross-linking analysis. J. Proteome Res. 6, 3412–3421 (2007).

    Article  CAS  Google Scholar 

  7. Leitner, A. et al. Probing native protein structures by chemical cross-linking, mass spectrometry, and bioinformatics. Mol. Cell Proteomics 9, 1634–1649 (2010).

    Article  CAS  Google Scholar 

  8. Liu, F. et al. Proteome-wide profiling of protein assemblies by cross-linking mass spectrometry. Nat. Methods 12, 1179–1184 (2015).

    Article  CAS  Google Scholar 

  9. Tan, D. et al. Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states. Elife 5, e12509 (2016).

    Article  Google Scholar 

  10. Schweppe, D. K. et al. Mitochondrial protein interactome elucidated by chemical cross-linking mass spectrometry. Proc. Natl. Acad. Sci. USA 114, 1732–1737 (2017).

    Article  CAS  Google Scholar 

  11. Liu, F. et al. The interactome of intact mitochondria by cross-linking mass spectrometry provides evidence for coexisting respiratory supercomplexes. Mol Cell Proteomics 17, 216–232 (2018).

    Article  CAS  Google Scholar 

  12. Fasci, D. et al. Histone interaction landscapes visualized by crosslinking mass spectrometry in intact cell nuclei. Mol. Cell Proteomics 17, 2018–2033 (2018).

    Article  CAS  Google Scholar 

  13. Wu, X. et al. Dynamic proteome response of Pseudomonas aeruginosa to tobramycin antibiotic treatment. Mol. Cell Proteomics 14, 2126–2137 (2015).

    Article  CAS  Google Scholar 

  14. Schweppe, D. K. et al. Host–microbe protein interactions during bacterial infection. Chem. Biol. 22, 1521–1530 (2015).

    Article  CAS  Google Scholar 

  15. Navare, A. T. et al. Probing the protein interaction network of Pseudomonas aeruginosa cells by chemical cross-linking mass spectrometry. Structure 23, 762–773 (2015).

    Article  CAS  Google Scholar 

  16. Weisbrod, C. R. et al. In vivo protein interaction network identified with a novel real-time cross-linked peptide identification strategy. J. Proteome Res. 12, 1569–1579 (2013).

    Article  CAS  Google Scholar 

  17. Chavez, J. D. et al. Protein interactions, post-translational modifications and topologies in human cells. Mol. Cell Proteomics 12, 1451–1467 (2013).

    Article  CAS  Google Scholar 

  18. Kaake, R. M. et al. A new in vivo cross-linking mass spectrometry platform to define protein–protein interactions in living cells. Mol. Cell Proteomics 13, 3533–3543 (2014).

    Article  CAS  Google Scholar 

  19. Wu, X. et al. In vivo protein interaction network analysis reveals porin-localized antibiotic inactivation in Acinetobacter baumannii strain AB5075. Nat. Commun. 7, 13414 (2016).

    Article  CAS  Google Scholar 

  20. Chavez, J. D. et al. Chemical crosslinking mass spectrometry analysis of protein conformations and supercomplexes in heart tissue. Cell Syst. 6, 136–141.e5 (2018).

    Article  CAS  Google Scholar 

  21. Klykov, O. et al. Efficient and robust proteome-wide approaches for cross-linking mass spectrometry. Nat. Protoc. 13, 2964–2990 (2010).

    Article  Google Scholar 

  22. Iacobucci, C. et al. A cross-linking/mass spectrometry workflow based on MS-cleavable cross-linkers and the MeroX software for studying protein structures and protein–protein interactions. Nat. Protoc. 13, 2864–2889 (2018).

    Article  CAS  Google Scholar 

  23. Orban-Nemeth, Z. et al. Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data. Nat. Protoc. 13, 478–494 (2018).

    Article  CAS  Google Scholar 

  24. Tang, X. et al. Mass spectrometry identifiable cross-linking strategy for studying protein–protein interactions. Anal. Chem. 77, 311–318 (2005).

    Article  CAS  Google Scholar 

  25. Tang, X. & Bruce, J. E. A new cross-linking strategy: protein interaction reporter (PIR) technology for protein–protein interaction studies. Mol. Biosyst. 6, 939–947 (2010).

    Article  CAS  Google Scholar 

  26. Petrotchenko, E. V. et al. BiPS, a photocleavable, isotopically coded, fluorescent cross-linker for structural proteomics. Mol. Cell Proteomics 8, 273–286 (2009).

    Article  CAS  Google Scholar 

  27. Yang, L. et al. In vivo application of photocleavable protein interaction reporter technology. J. Proteome Res. 11, 1027–1041 (2012).

    Article  CAS  Google Scholar 

  28. Zheng, Q. et al. Probing protein 3D structures and conformational changes using electrochemistry-assisted isotope labeling cross-linking mass spectrometry. J. Am. Soc. Mass. Spectrom. 27, 864–875 (2016).

    Article  CAS  Google Scholar 

  29. Zheng, Q. et al. Cross-linking electrochemical mass spectrometry for probing protein three-dimensional structures. Anal. Chem. 86, 8983–8991 (2014).

    Article  CAS  Google Scholar 

  30. Kao, A. et al. Development of a novel cross-linking strategy for fast and accurate identification of cross-linked peptides of protein complexes. Mol. Cell Proteomics 10, M110.002212 (2011).

    Article  Google Scholar 

  31. Muller, M. Q. et al. A universal matrix-assisted laser desorption/ionization cleavable cross-linker for protein structure analysis. Rapid Commun. Mass. Spectrom. 25, 155–161 (2011).

    Article  Google Scholar 

  32. Petrotchenko, E. V., Serpa, J. J. & Borchers, C. H. An isotopically coded CID-cleavable biotinylated cross-linker for structural proteomics. Mol. Cell Proteomics 10, M110.001420 (2011).

    Article  Google Scholar 

  33. Tang, X. et al. Profiling the membrane proteome of Shewanella oneidensis MR-1 with new affinity labeling probes. J. Proteome Res. 6, 724–734 (2007).

    Article  CAS  Google Scholar 

  34. Schmidt, C. & Urlaub, H. Combining cryo-electron microscopy (cryo-EM) and cross-linking mass spectrometry (CX-MS) for structural elucidation of large protein assemblies. Curr. Opin. Struct. Biol. 46, 157–168 (2017).

    Article  CAS  Google Scholar 

  35. Rinner, O. et al. Identification of cross-linked peptides from large sequence databases. Nat. Methods 5, 315–318 (2008).

    Article  CAS  Google Scholar 

  36. Mohr, J. P. et al. Mango: a general tool for collision induced dissociation-cleavable cross-linked peptide identification. Anal. Chem. 90, 6028–6034 (2018).

    Article  CAS  Google Scholar 

  37. Zheng, C., Perumalla, P., Chavez, J. D., Eng, J. K. & Bruce, J. E. XLink-DB: database and software tools for storing and visualizing protein interaction topology data. J. Proteome Res. 12, 1989–1995 (2013).

    Article  CAS  Google Scholar 

  38. Schweppe, D. K. et al. XLinkDB 2.0: integrated, large-scale structural analysis of protein crosslinking data. Bioinformatics 32, 2716–2718 (2016).

    Article  CAS  Google Scholar 

  39. Keller, A., Chavez, J. D., Eng, J. K., Thornton, Z. & Bruce, J. E. Tools for 3D interactome visualization. J. Proteome Res. 18, 753–758 (2018).

    Article  Google Scholar 

  40. Chavez, J. D. et al. A general method for targeted quantitative cross-linking mass spectrometry. PLoS ONE 11, e0167547 (2016).

    Article  Google Scholar 

  41. Chavez, J. D., Schweppe, D. K., Eng, J. K. & Bruce, J. E. In vivo conformational dynamics of Hsp90 and its interactors. Cell Chem. Biol. 23, 716–726 (2016).

    Article  CAS  Google Scholar 

  42. Chavez, J. D. et al. Quantitative interactome analysis reveals a chemoresistant edgotype. Nat. Commun. 6, 7928 (2015).

    Article  CAS  Google Scholar 

  43. Zhong, X. et al. Large-scale and targeted quantitative cross-linking MS using isotope-labeled protein interaction reporter (PIR) cross-linkers. J. Proteome Res. 16, 720–727 (2016).

    Article  Google Scholar 

  44. Chavez, J. D. et al. Cross-linking measurements of the potato leafroll virus reveal protein interaction topologies required for virion stability, aphid transmission, and virus–plant interactions. J. Proteome Res. 11, 2968–2981 (2012).

    Article  CAS  Google Scholar 

  45. DeBlasio, S. L. et al. Visualization of host–polerovirus interaction topologies using protein interaction reporter technology. J. Virol. 90, 1973–1987 (2016).

    Article  CAS  Google Scholar 

  46. Alexander, M. M. et al. Insights in luteovirid structural biology guided by chemical cross-linking and high resolution mass spectrometry. Virus Res. 241, 42–52 (2017).

    Article  CAS  Google Scholar 

  47. Zhang, H. et al. In vivo identification of the outer membrane protein OmcA–MtrC interaction network in Shewanella oneidensis MR-1 cells using novel hydrophobic chemical cross-linkers. J. Proteome Res. 7, 1712–1720 (2008).

    Article  CAS  Google Scholar 

  48. Ramsey, J. S. et al. Protein interaction networks at the host-microbe interface in Diaphorina citri, the insect vector of the citrus greening pathogen. R. Soc. Open Sci. 4, 160545 (2017).

    Article  CAS  Google Scholar 

  49. Zheng, C. et al. Cross-linking measurements of in vivo protein complex topologies. Mol. Cell Proteomics 10, M110.006841 (2011).

    Article  Google Scholar 

  50. Rozbesky, D. et al. Impact of chemical cross-linking on protein structure and function. Anal. Chem. 90, 1104–1113 (2018).

    Article  CAS  Google Scholar 

  51. Ding, Y. H. et al. Modeling protein excited-state structures from “over-length” chemical cross-links. J. Biol. Chem. 292, 1187–1196 (2017).

    Article  CAS  Google Scholar 

  52. Singh, P., Panchaud, A. & Goodlett, D. R. Chemical cross-linking and mass spectrometry as a low-resolution protein structure determination technique. Anal. Chem. 82, 2636–2642 (2010).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  54. Fischer, L. & Rappsilber, J. Quirks of error estimation in cross-linking/mass spectrometry. Anal. Chem. 89, 3829–3833 (2017).

    Article  CAS  Google Scholar 

  55. Trnka, M. J. et al. Matching cross-linked peptide spectra: only as good as the worse identification. Mol. Cell Proteomics 13, 420–434 (2014).

    Article  CAS  Google Scholar 

  56. Keller, A., Chavez, J. D. & Bruce, J. E. Increased sensitivity with automated validation of XL-MS cleavable peptide crosslinks. Bioinformatics 35, 895–897 (2019).

    Article  Google Scholar 

  57. Keller, A. et al. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 1, 2005.0017 (2005).

    Article  Google Scholar 

  58. Keller, A. et al. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002).

    Article  CAS  Google Scholar 

  59. Shteynberg, D. et al. iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol. Cell Proteomics 10, M111.007690 (2011).

    Article  Google Scholar 

  60. Kulak, N. A. et al. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat. Methods 11, 319–324 (2014).

    Article  CAS  Google Scholar 

  61. Liang, K. et al. Cryo-EM structure of human mitochondrial trifunctional protein. Proc. Natl. Acad. Sci. USA 115, 7039–7044 (2018).

    Article  CAS  Google Scholar 

  62. Jacobs, A. C. et al. AB5075, a highly virulent isolate of Acinetobacter baumannii, as a model strain for the evaluation of pathogenesis and antimicrobial treatments. MBio 5, e01076-14 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

We thank members of the Bruce Lab for helpful discussions and suggestions. This work was supported by the following NIH grants: R01GM086688 to J.E.B., J.D.C., and A.K.; R01GM097112 to J.E.B. and J.P.M.; U19AI107775 to J.E.B., X.Z., and A.K.; R01HL110349 to J.E.B. and J.D.C.; and R01HL142628 to J.E.B. and J.D.C.

Author information

Authors and Affiliations

Authors

Contributions

J.D.C., J.P.M., M.M., X.Z., A.K., and J.E.B. contributed to the development of this protocol. J.D.C., J.P.M., and J.E.B. wrote and edited the manuscript.

Corresponding author

Correspondence to James E. Bruce.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nature Protocols thanks Andrea Sinz and other anonymous reviewer(s) for their contribution to the peer review of this work.

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

Related links

Key references using this protocol

Chavez, J. D., Schweppe, D. K., Eng, J. K., & Bruce, J. E. Cell Chem. Biol. 23, 716–726 (2016): https://doi.org/10.1016/j.chembiol.2016.05.012

Wu, X. et al. Nat. Commun. 7, 13414 (2016): https://doi.org/10.1038/ncomms13414

Mohr, J. P., Perumalla, P., Chavez, J. D., Eng, J. K., & Bruce, J. E. Anal. Chem. 90, 6028–6034 (2018): https://doi.org/10.1021/acs.analchem.7b04991

Keller, A., Chavez, J. D., & Bruce, J. E. Bioinformatics 35, 895–897 (2019): https://doi.org/10.1093/bioinformatics/bty720

Keller, A., Chavez, J. D., Eng, J. K., Thornton, Z., & Bruce, J. E. J. Proteome Res. 18, 753–758 (2019): https://doi.org/10.1021/acs.jproteome.8b00703

Key data used in this protocol

Chavez, J. D. et al. Cell Syst. 6, 136–141.e5 (2018): https://doi.org/10.1016/j.cels.2017.10.017

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chavez, J.D., Mohr, J.P., Mathay, M. et al. Systems structural biology measurements by in vivo cross-linking with mass spectrometry. Nat Protoc 14, 2318–2343 (2019). https://doi.org/10.1038/s41596-019-0181-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-019-0181-3

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing