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.

  • Primer
  • Published:

Proteomic strategies for characterizing ubiquitin-like modifications

Abstract

The modification of proteins by the addition of ubiquitin and other ubiquitin-like proteins (UBLs) is involved in a wide range of cellular processes including cell cycle progression, the DNA damage response, endocytosis, cell signalling, autophagy and protein quality control. The UBL family comprises more than a dozen structurally related members, with ubiquitin, small ubiquitin-like modifier (SUMO) proteins, NEDD8, ISG15 and FAT10 being the most commonly known. Each UBL is associated with a distinct set of enzymes that alter the architecture and fate of their cognate proteins. UBL-conjugating enzymes add one or more UBLs to lysine and non-lysine acceptor sites on their target proteins, forming a complex distribution of monomeric and polymeric modifications. Different approaches and strategies are available to identify the sites of UBL modification, the types of modification and their dynamics upon various cellular stimuli; these techniques can decipher the complex architecture of UBL substrates and expand our understanding of UBL functions and their importance in cellular homeostasis and human diseases. This Primer covers the current methods for identifying UBL substrates, their modification sites and UBL chain linkages, and describes where the application of these methods can be used to gain biological insights into UBL functions.

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: Overview of the UBL conjugation machinery and information available from UBL proteomics experiments.
Fig. 2: Identification of targets of UBLs using strategies that exogenously express UBLs or related enzymes.
Fig. 3: Identification of ubiquitin or UBLs using strategies that enrich endogenous UBL proteins.
Fig. 4: Quantitative proteomic strategies applied to UBL proteomics.
Fig. 5: Data analysis workflow and example of MS/MS spectra.
Fig. 6: Crosstalk between ubiquitin and UBLs.
Fig. 7: Roles of UBL modifications in the cell.

Similar content being viewed by others

References

  1. Rajalingam, K. & Dikic, I. SnapShot: expanding the ubiquitin code. Cell 164, 1074–1074.e1 (2016).

    Google Scholar 

  2. Yau, R. & Rape, M. The increasing complexity of the ubiquitin code. Nat. Cell Biol. 18, 579–586 (2016). This review highlights the diversity of ubiquitin chain topologies and their impact on cell signalling.

    Google Scholar 

  3. Kerscher, O., Felberbaum, R. & Hochstrasser, M. Modification of proteins by ubiquitin and ubiquitin-like proteins. Annu. Rev. Cell Dev. Biol. 22, 159–180 (2006).

    Google Scholar 

  4. Cappadocia, L. & Lima, C. D. Ubiquitin-like protein conjugation: structures, chemistry, and mechanism. Chem. Rev. 118, 889–918 (2018). This Review describes the conjugation machinery of UBLs and different approaches to identify intermediates during UBL activation and conjugation.

    Google Scholar 

  5. Clague, M. J., Urbe, S. & Komander, D. Breaking the chains: deubiquitylating enzyme specificity begets function. Nat. Rev. Mol. Cell Biol. 20, 338–352 (2019).

    Google Scholar 

  6. Ronau, J. A., Beckmann, J. F. & Hochstrasser, M. Substrate specificity of the ubiquitin and Ubl proteases. Cell Res. 26, 441–456 (2016).

    Google Scholar 

  7. McClellan, A. J., Laugesen, S. H. & Ellgaard, L. Cellular functions and molecular mechanisms of non-lysine ubiquitination. Open. Biol. 9, 190147 (2019).

    Google Scholar 

  8. Jansen, N. S. & Vertegaal, A. C. O. A chain of events: regulating target proteins by SUMO polymers. Trends Biochem. Sci. 46, 113–123 (2021).

    Google Scholar 

  9. Bailly, A. P. et al. The balance between mono- and NEDD8-chains controlled by NEDP1 upon DNA damage is a regulatory module of the HSP70 ATPase activity. Cell Rep. 29, 212–224.e8 (2019).

    Google Scholar 

  10. Keuss, M. J. et al. Unanchored tri-NEDD8 inhibits PARP-1 to protect from oxidative stress-induced cell death. EMBO J. 38, e100024 (2019).

    Google Scholar 

  11. Perez Berrocal, D. A., Witting, K. F., Ovaa, H. & Mulder, M. P. C. Hybrid chains: a collaboration of ubiquitin and ubiquitin-like modifiers introducing cross-functionality to the ubiquitin code. Front. Chem. 7, 931 (2019).

    ADS  Google Scholar 

  12. Streich, F. C. Jr. & Lima, C. D. Structural and functional insights to ubiquitin-like protein conjugation. Annu. Rev. Biophys. 43, 357–379 (2014).

    Google Scholar 

  13. van der Veen, A. G. & Ploegh, H. L. Ubiquitin-like proteins. Annu. Rev. Biochem. 81, 323–357 (2012).

    Google Scholar 

  14. Akutsu, M., Dikic, I. & Bremm, A. Ubiquitin chain diversity at a glance. J. Cell Sci. 129, 875–880 (2016). This paper presents a method to unravel the complex architecture of ubiquitin chains to understand polyubiquitin signals.

    Google Scholar 

  15. Swatek, K. N. et al. Insights into ubiquitin chain architecture using Ub-clipping. Nature 572, 533–537 (2019).

    ADS  Google Scholar 

  16. El-Asmi, F. et al. Cross-talk between SUMOylation and ISGylation in response to interferon. Cytokine 129, 155025 (2020).

    Google Scholar 

  17. Aichem, A. et al. The proteomic analysis of endogenous FAT10 substrates identifies p62/SQSTM1 as a substrate of FAT10ylation. J. Cell Sci. 125, 4576–4585 (2012).

    Google Scholar 

  18. McManus, F. P., Lamoliatte, F. & Thibault, P. Identification of cross talk between SUMOylation and ubiquitylation using a sequential peptide immunopurification approach. Nat. Protoc. 12, 2342–2358 (2017).

    Google Scholar 

  19. Udeshi, N. D., Mertins, P., Svinkina, T. & Carr, S. A. Large-scale identification of ubiquitination sites by mass spectrometry. Nat. Protoc. 8, 1950–1960 (2013).

    Google Scholar 

  20. Hendriks, I. A. & Vertegaal, A. C. A high-yield double-purification proteomics strategy for the identification of SUMO sites. Nat. Protoc. 11, 1630–1649 (2016).

    Google Scholar 

  21. Barysch, S. V., Dittner, C., Flotho, A., Becker, J. & Melchior, F. Identification and analysis of endogenous SUMO1 and SUMO2/3 targets in mammalian cells and tissues using monoclonal antibodies. Nat. Protoc. 9, 896–909 (2014).

    Google Scholar 

  22. Liu, N. et al. Clinically used antirheumatic agent auranofin is a proteasomal deubiquitinase inhibitor and inhibits tumor growth. Oncotarget 5, 5453–5471 (2014).

    Google Scholar 

  23. Liu, N. et al. A novel proteasome inhibitor suppresses tumor growth via targeting both 19S proteasome deubiquitinases and 20S proteolytic peptidases. Sci. Rep. 4, 5240 (2014).

    Google Scholar 

  24. Zhang, J. J., Ng, K. M., Lok, C. N., Sun, R. W. & Che, C. M. Deubiquitinases as potential anti-cancer targets for gold(III) complexes. Chem. Commun. 49, 5153–5155 (2013).

    Google Scholar 

  25. Graham, J. M. Fractionation of subcellular organelles. Curr. Protoc. Cell Biol. 69, 3.1.1–3.1.22 (2015).

    Google Scholar 

  26. Wagner, S. A. et al. Proteomic analyses reveal divergent ubiquitylation site patterns in murine tissues. Mol. Cell Proteom. 11, 1578–1585 (2012).

    Google Scholar 

  27. McManus, F. P., Altamirano, C. D. & Thibault, P. In vitro assay to determine SUMOylation sites on protein substrates. Nat. Protoc. 11, 387–397 (2016).

    Google Scholar 

  28. Jeram, S. M., Srikumar, T., Pedrioli, P. G. & Raught, B. Using mass spectrometry to identify ubiquitin and ubiquitin-like protein conjugation sites. Proteomics 9, 922–934 (2009).

    Google Scholar 

  29. Argenzio, E. et al. Proteomic snapshot of the EGF-induced ubiquitin network. Mol. Syst. Biol. 7, 462 (2011).

    Google Scholar 

  30. Danielsen, J. M. et al. Mass spectrometric analysis of lysine ubiquitylation reveals promiscuity at site level. Mol. Cell Proteom. 10, M110 003590 (2011).

    Google Scholar 

  31. Peng, J. et al. A proteomics approach to understanding protein ubiquitination. Nat. Biotechnol. 21, 921–926 (2003).

    Google Scholar 

  32. Roux, K. J., Kim, D. I., Burke, B. & May, D. G. BioID: a screen for protein–protein interactions. Curr. Protoc. Protein Sci. 91, 19.23.11–19.23.15 (2018).

    Google Scholar 

  33. Hill, Z. B., Pollock, S. B., Zhuang, M. & Wells, J. A. Direct proximity tagging of small molecule protein targets using an engineered NEDD8 ligase. J. Am. Chem. Soc. 138, 13123–13126 (2016).

    Google Scholar 

  34. Coyaud, E. et al. BioID-based identification of Skp Cullin F-box (SCF)β–TrCP1/2 E3 ligase substrates. Mol. Cell Proteom. 14, 1781–1795 (2015).

    Google Scholar 

  35. Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat. Biotechnol. 36, 880–887 (2018).

    Google Scholar 

  36. Hendriks, I. A. et al. Uncovering global SUMOylation signaling networks in a site-specific manner. Nat. Struct. Mol. Biol. 21, 927–936 (2014). This paper describes a method to purify SUMOylated proteins, enabling the identification of more than 4,300 SUMOylation sites in human cells.

    Google Scholar 

  37. Impens, F., Radoshevich, L., Cossart, P. & Ribet, D. Mapping of SUMO sites and analysis of SUMOylation changes induced by external stimuli. Proc. Natl Acad. Sci. USA 111, 12432–12437 (2014).

    ADS  Google Scholar 

  38. Lamoliatte, F. et al. Large-scale analysis of lysine SUMOylation by SUMO remnant immunoaffinity profiling. Nat. Commun. 5, 5409 (2014).

    ADS  Google Scholar 

  39. Tammsalu, T. et al. Proteome-wide identification of SUMO2 modification sites. Sci. Signal. 7, rs2 (2014).

    Google Scholar 

  40. O’Connor, H. F. et al. Ubiquitin-activated interaction traps (UBAITs) identify E3 ligase binding partners. EMBO Rep. 16, 1699–1712 (2015).

    Google Scholar 

  41. Kumar, R., Gonzalez-Prieto, R., Xiao, Z., Verlaan-de Vries, M. & Vertegaal, A. C. O. The STUbL RNF4 regulates protein group SUMOylation by targeting the SUMO conjugation machinery. Nat. Commun. 8, 1809 (2017).

    ADS  Google Scholar 

  42. Hjerpe, R. et al. Efficient protection and isolation of ubiquitylated proteins using tandem ubiquitin-binding entities. EMBO Rep. 10, 1250–1258 (2009).

    Google Scholar 

  43. Yoshida, Y. et al. A comprehensive method for detecting ubiquitinated substrates using TR-TUBE. Proc. Natl Acad. Sci. USA 112, 4630–4635 (2015).

    ADS  Google Scholar 

  44. Gao, Y. et al. Enhanced purification of ubiquitinated proteins by engineered tandem hybrid ubiquitin-binding domains (ThUBDs). Mol. Cell Proteom. 15, 1381–1396 (2016).

    Google Scholar 

  45. Lang, V., Da Silva-Ferrada, E., Barrio, R., Sutherland, J. D. & Rodriguez, M. S. Using biotinylated SUMO-traps to analyze SUMOylated proteins. Methods Mol. Biol. 1475, 109–121 (2016).

    Google Scholar 

  46. Da Silva-Ferrada, E. et al. Analysis of SUMOylated proteins using SUMO-traps. Sci. Rep. 3, 1690 (2013).

    Google Scholar 

  47. Zhuang, M., Guan, S., Wang, H., Burlingame, A. L. & Wells, J. A. Substrates of IAP ubiquitin ligases identified with a designed orthogonal E3 ligase, the NEDDylator. Mol. Cell 49, 273–282 (2013).

    Google Scholar 

  48. Salas-Lloret, D., Agabitini, G. & Gonzalez-Prieto, R. TULIP2: an improved method for the identification of ubiquitin E3-specific targets. Front. Chem. 7, 802 (2019).

    ADS  Google Scholar 

  49. Khoshnood, B., Dacklin, I. & Grabbe, C. A proteomics approach to identify targets of the ubiquitin-like molecule Urm1 in Drosophila melanogaster. PLoS ONE 12, e0185611 (2017).

    Google Scholar 

  50. Leng, L. et al. A proteomics strategy for the identification of FAT10-modified sites by mass spectrometry. J. Proteome Res. 13, 268–276 (2014).

    Google Scholar 

  51. Yoo, H. M. et al. Modification of ASC1 by UFM1 is crucial for ERα transactivation and breast cancer development. Mol. Cell 56, 261–274 (2014).

    Google Scholar 

  52. Bakos, G. et al. An E2-ubiquitin thioester-driven approach to identify substrates modified with ubiquitin and ubiquitin-like molecules. Nat. Commun. 9, 4776 (2018).

    ADS  Google Scholar 

  53. Uzoma, I. et al. Global identification of small ubiquitin-related modifier (SUMO) substrates reveals crosstalk between SUMOylation and phosphorylation promotes cell migration. Mol. Cell Proteom. 17, 871–888 (2018).

    Google Scholar 

  54. Merbl, Y., Refour, P., Patel, H., Springer, M. & Kirschner, M. W. Profiling of ubiquitin-like modifications reveals features of mitotic control. Cell 152, 1160–1172 (2013).

    Google Scholar 

  55. Oh, Y. H. et al. Chip-based analysis of SUMO (small ubiquitin-like modifier) conjugation to a target protein. Biosens. Bioelectron. 22, 1260–1267 (2007).

    Google Scholar 

  56. Akimov, V. et al. UbiSite approach for comprehensive mapping of lysine and N-terminal ubiquitination sites. Nat. Struct. Mol. Biol. 25, 631–640 (2018). This paper presents the UbiSite immunoaffinity method to specifically enrich and identify ubiquitin-modified proteins uncovering more than 63,000 ubiquitylation sites on 9,200 proteins.

    Google Scholar 

  57. Xu, P. & Peng, J. Characterization of polyubiquitin chain structure by middle-down mass spectrometry. Anal. Chem. 80, 3438–3444 (2008).

    Google Scholar 

  58. Vogl, A. M. et al. Global site-specific neddylation profiling reveals that NEDDylated cofilin regulates actin dynamics. Nat. Struct. Mol. Biol. 27, 210–220 (2020).

    Google Scholar 

  59. Hendriks, I. A. et al. Site-specific characterization of endogenous SUMOylation across species and organs. Nat. Commun. 9, 2456 (2018).

    ADS  Google Scholar 

  60. Rinfret Robert, C., McManus, F. P., Lamoliatte, F. & Thibault, P. Interplay of ubiquitin-like modifiers following arsenic trioxide treatment. J. Proteome Res. 19, 1999–2010 (2020).

    Google Scholar 

  61. Lamoliatte, F., McManus, F. P., Maarifi, G., Chelbi-Alix, M. K. & Thibault, P. Uncovering the SUMOylation and ubiquitylation crosstalk in human cells using sequential peptide immunopurification. Nat. Commun. 8, 14109 (2017). This paper uses a dual-remnant immunoaffinity enrichment to identify protein SUMOylation and ubiquitylation in a site-specific manner.

    ADS  Google Scholar 

  62. Fulzele, A. & Bennett, E. J. Ubiquitin diGLY proteomics as an approach to identify and quantify the ubiquitin-modified proteome. Methods Mol. Biol. 1844, 363–384 (2018).

    Google Scholar 

  63. Lumpkin, R. J. et al. Site-specific identification and quantitation of endogenous SUMO modifications under native conditions. Nat. Commun. 8, 1171 (2017). This paper features a method to enrich endogenous SUMOylated proteins and their identification by mass spectrometry, revealing more than 1,200 SUMO sites.

    ADS  Google Scholar 

  64. Cai, L. et al. Proteome-wide mapping of endogenous SUMOylation sites in mouse testis. Mol. Cell Proteom. 16, 717–727 (2017).

    Google Scholar 

  65. Ludwig, C. et al. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol. Syst. Biol. 14, e8126 (2018).

    Google Scholar 

  66. Hansen, F. M. et al. Data-independent acquisition method for ubiquitinome analysis reveals regulation of circadian biology. Nat. Commun. 12, 254 (2021).

    Google Scholar 

  67. Calderon-Celis, F., Encinar, J. R. & Sanz-Medel, A. Standardization approaches in absolute quantitative proteomics with mass spectrometry. Mass. Spectrom. Rev. 37, 715–737 (2018).

    ADS  Google Scholar 

  68. Cox, J. & Mann, M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu. Rev. Biochem. 80, 273–299 (2011).

    Google Scholar 

  69. Ong, S. E. & Mann, M. Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 1, 252–262 (2005).

    Google Scholar 

  70. Rodriguez-Suarez, E. & Whetton, A. D. The application of quantification techniques in proteomics for biomedical research. Mass. Spectrom. Rev. 32, 1–26 (2013).

    ADS  Google Scholar 

  71. Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell Proteom. 13, 2513–2526 (2014).

    Google Scholar 

  72. Lear, T. B. et al. Kelch-like protein 42 is a profibrotic ubiquitin E3 ligase involved in systemic sclerosis. J. Biol. Chem. 295, 4171–4180 (2020).

    Google Scholar 

  73. Zhu, W., Smith, J. W. & Huang, C. M. Mass spectrometry-based label-free quantitative proteomics. J. Biomed. Biotechnol. 2010, 840518 (2010).

    Google Scholar 

  74. Hogrebe, A. et al. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat. Commun. 9, 1045 (2018).

    ADS  Google Scholar 

  75. Ong, S. E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell Proteom. 1, 376–386 (2002).

    Google Scholar 

  76. An, J. et al. pSILAC mass spectrometry reveals ZFP91 as IMiD-dependent substrate of the CRL4(CRBN) ubiquitin ligase. Nat. Commun. 8, 15398 (2017).

    ADS  Google Scholar 

  77. Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J. R. & Mann, M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 7, 383–385 (2010).

    Google Scholar 

  78. Wiese, S., Reidegeld, K. A., Meyer, H. E. & Warscheid, B. Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics 7, 340–350 (2007).

    Google Scholar 

  79. Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003).

    Google Scholar 

  80. Rose, C. M. et al. Highly multiplexed quantitative mass spectrometry analysis of ubiquitylomes. Cell Syst. 3, 395–403.e4 (2016).

    Google Scholar 

  81. Niu, M. et al. Extensive peptide fractionation and y1 ion-based interference detection method for enabling accurate quantification by isobaric labeling and mass spectrometry. Anal. Chem. 89, 2956–2963 (2017).

    Google Scholar 

  82. Wuhr, M. et al. Accurate multiplexed proteomics at the MS2 level using the complement reporter ion cluster. Anal. Chem. 84, 9214–9221 (2012).

    Google Scholar 

  83. Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937–940 (2011).

    Google Scholar 

  84. Udeshi, N. D. et al. Rapid and deep-scale ubiquitylation profiling for biology and translational research. Nat. Commun. 11, 359 (2020).

    ADS  Google Scholar 

  85. Perkins, D. N., Pappin, D. J., Creasy, D. M. & Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).

    Google Scholar 

  86. 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).

    Google Scholar 

  87. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Google Scholar 

  88. Zhang, J. et al. PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification. Mol. Cell Proteom. 11, M111 010587 (2012).

    Google Scholar 

  89. Li, Y. et al. An integrated bioinformatics platform for investigating the human E3 ubiquitin ligase–substrate interaction network. Nat. Commun. 8, 347 (2017).

    ADS  Google Scholar 

  90. Xue, Y., Zhou, F., Fu, C., Xu, Y. & Yao, X. SUMOsp: a web server for sumoylation site prediction. Nucleic Acids Res. 34, W254–W257 (2006).

    Google Scholar 

  91. Ren, J. et al. Systematic study of protein sumoylation: development of a site-specific predictor of SUMOsp 2.0. Proteomics 9, 3409–3412 (2009).

    Google Scholar 

  92. Xue, Y. et al. GPS: a comprehensive www server for phosphorylation sites prediction. Nucleic Acids Res. 33, W184–W187 (2005).

    Google Scholar 

  93. Schwartz, D. & Gygi, S. P. An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nat. Biotechnol. 23, 1391–1398 (2005).

    Google Scholar 

  94. Zhao, Q. et al. GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs. Nucleic Acids Res. 42, W325–W330 (2014).

    Google Scholar 

  95. Beauclair, G., Bridier-Nahmias, A., Zagury, J. F., Saib, A. & Zamborlini, A. JASSA: a comprehensive tool for prediction of SUMOylation sites and SIMs. Bioinformatics 31, 3483–3491 (2015).

    Google Scholar 

  96. Dehzangi, A., Lopez, Y., Taherzadeh, G., Sharma, A. & Tsunoda, T. SumSec: accurate prediction of sumoylation sites using predicted secondary structure. Molecules 23, 3260 (2018).

    Google Scholar 

  97. Sharma, A. et al. HseSUMO: sumoylation site prediction using half-sphere exposures of amino acids residues. BMC Genomics 19, 982 (2019).

    Google Scholar 

  98. Qian, Y., Ye, S., Zhang, Y. & Zhang, J. SUMO-Forest: a cascade forest based method for the prediction of SUMOylation sites on imbalanced data. Gene 741, 144536 (2020).

    Google Scholar 

  99. Xu, H. D., Liang, R. P., Wang, Y. G. & Qiu, J. D. mUSP: a high-accuracy map of the in situ crosstalk of ubiquitylation and SUMOylation proteome predicted via the feature enhancement approach. Brief. Bioinform. 22, bbaa050 (2021).

    Google Scholar 

  100. Qiu, W., Xu, C., Xiao, X. & Xu, D. Computational prediction of ubiquitination proteins using evolutionary profiles and functional domain annotation. Curr. Genomics 20, 389–399 (2019).

    Google Scholar 

  101. He, F. et al. Large-scale prediction of protein ubiquitination sites using a multimodal deep architecture. BMC Syst. Biol. 12, 109 (2018).

    Google Scholar 

  102. Yavuz, A. S., Sozer, N. B. & Sezerman, O. U. Prediction of neddylation sites from protein sequences and sequence-derived properties. BMC Bioinformatics 16 (Suppl. 18), S9 (2015).

    Google Scholar 

  103. Hornbeck, P. V. et al. PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res. 40, D261–D270 (2012).

    Google Scholar 

  104. Tammsalu, T. et al. Proteome-wide identification of SUMO modification sites by mass spectrometry. Nat. Protoc. 10, 1374–1388 (2015).

    Google Scholar 

  105. Cox, J. et al. A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat.Protoc. 4, 698–705 (2009).

    Google Scholar 

  106. Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).

    Google Scholar 

  107. Ma, B. et al. PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun. Mass. Spectrom. 17, 2337–2342 (2003).

    ADS  Google Scholar 

  108. Hendriks, I. A. et al. Site-specific mapping of the human SUMO proteome reveals co-modification with phosphorylation. Nat. Struct. Mol. Biol. 24, 325–336 (2017).

    Google Scholar 

  109. Chachami, G. et al. Hypoxia-induced changes in SUMO conjugation affect transcriptional regulation under low oxygen. Mol. Cell Proteom. 18, 1197–1209 (2019).

    Google Scholar 

  110. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    Google Scholar 

  111. Fu, H., Yang, Y., Wang, X., Wang, H. & Xu, Y. DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins. BMC Bioinformatics 20, 86 (2019).

    Google Scholar 

  112. Liu, Y., Li, A., Zhao, X. M. & Wang, M. DeepTL-Ubi: a novel deep transfer learning method for effectively predicting ubiquitination sites of multiple species. Methods 192, 103–111 (2020).

    Google Scholar 

  113. The Gene Ontology Consortium. The Gene Ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).

    Google Scholar 

  114. Eifler, K. et al. SUMO targets the APC/C to regulate transition from metaphase to anaphase. Nat. Commun. 9, 1119 (2018).

    ADS  Google Scholar 

  115. El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2019).

    Google Scholar 

  116. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    Google Scholar 

  117. Psakhye, I. & Jentsch, S. Protein group modification and synergy in the SUMO pathway as exemplified in DNA repair. Cell 151, 807–820 (2012). This paper highlights the concept of the simultaneous modification of related sets of target proteins to regulate cellular processes by PTM.

    Google Scholar 

  118. Schwertman, P., Bekker-Jensen, S. & Mailand, N. Regulation of DNA double-strand break repair by ubiquitin and ubiquitin-like modifiers. Nat. Rev. Mol. Cell Biol. 17, 379–394 (2016).

    Google Scholar 

  119. Jackson, S. P. & Durocher, D. Regulation of DNA damage responses by ubiquitin and SUMO. Mol.Cell 49, 795–807 (2013).

    Google Scholar 

  120. Odeh, H. M., Coyaud, E., Raught, B. & Matunis, M. J. The SUMO-specific isopeptidase SENP2 is targeted to intracellular membranes via a predicted N-terminal amphipathic α-helix. Mol. Biol. Cell 29, 1878–1890 (2018).

    Google Scholar 

  121. Xu, P. et al. Quantitative proteomics reveals the function of unconventional ubiquitin chains in proteasomal degradation. Cell 137, 133–145 (2009).

    Google Scholar 

  122. Kim, W. et al. Systematic and quantitative assessment of the ubiquitin-modified proteome. Mol. Cell 44, 325–340 (2011).

    Google Scholar 

  123. Xu, G., Paige, J. S. & Jaffrey, S. R. Global analysis of lysine ubiquitination by ubiquitin remnant immunoaffinity profiling. Nat. Biotechnol. 28, 868–873 (2010). This study is the first to report on the use of diglycine remnant immunopurification for large-scale identification of ubiquitin-modified proteomes.

    Google Scholar 

  124. Zhang, Y. et al. The in vivo ISGylome links ISG15 to metabolic pathways and autophagy upon Listeria monocytogenes infection. Nat. Commun. 10, 5383 (2019).

    ADS  Google Scholar 

  125. Pinto-Fernandez, A. et al. Deletion of the deISGylating enzyme USP18 enhances tumour cell antigenicity and radiosensitivity. Br. J. Cancer 124, 817–830 (2021).

    Google Scholar 

  126. Steen, H. & Mann, M. The ABC’s (and XYZ’s) of peptide sequencing. Nat. Rev. Mol. Cell Biol. 5, 699–711 (2004).

    Google Scholar 

  127. Na, C. H. et al. Synaptic protein ubiquitination in rat brain revealed by antibody-based ubiquitome analysis. J. Proteome Res. 11, 4722–4732 (2012).

    Google Scholar 

  128. Griffin, N. M. et al. Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat. Biotechnol. 28, 83–89 (2010).

    Google Scholar 

  129. Murie, C. et al. Normalization of mass spectrometry data (NOMAD). Adv. Biol. Regul. 67, 128–133 (2018).

    Google Scholar 

  130. Vertegaal, A. C. Uncovering ubiquitin and ubiquitin-like signaling networks. Chem. Rev. 111, 7923–7940 (2011).

    Google Scholar 

  131. Aichem, A. et al. The ubiquitin-like modifier FAT10 interferes with SUMO activation. Nat. Commun. 10, 4452 (2019).

    ADS  Google Scholar 

  132. French, M. E., Koehler, C. F. & Hunter, T. Emerging functions of branched ubiquitin chains. Cell Discov. 7, 6 (2021).

    Google Scholar 

  133. Haakonsen, D. L. & Rape, M. Branching out: improved signaling by heterotypic ubiquitin chains. Trends Cell Biol. 29, 704–716 (2019).

    Google Scholar 

  134. Kane, L. A. et al. PINK1 phosphorylates ubiquitin to activate Parkin E3 ubiquitin ligase activity. J. Cell Biol. 205, 143–153 (2014).

    Google Scholar 

  135. Kazlauskaite, A. et al. Parkin is activated by PINK1-dependent phosphorylation of ubiquitin at Ser65. Biochem. J. 460, 127–139 (2014).

    Google Scholar 

  136. Koyano, F. et al. Ubiquitin is phosphorylated by PINK1 to activate parkin. Nature 510, 162–166 (2014). In this study, phosphorylation of ubiquitin at Ser65 by PINK1 is found to enhance ubiquitin conjugation by the E3 ligase Parkin.

    ADS  Google Scholar 

  137. Ordureau, A. et al. Dynamics of PARKIN-dependent mitochondrial ubiquitylation in induced neurons and model systems revealed by digital snapshot proteomics. Mol. Cell 70, 211–227.e8 (2018).

    Google Scholar 

  138. Wauer, T. et al. Ubiquitin Ser65 phosphorylation affects ubiquitin structure, chain assembly and hydrolysis. EMBO J. 34, 307–325 (2015).

    Google Scholar 

  139. Baek, K. et al. NEDD8 nucleates a multivalent Cullin–RING–UBE2D ubiquitin ligation assembly. Nature 578, 461–466 (2020).

    ADS  Google Scholar 

  140. Da Costa, I. C. & Schmidt, C. K. Ubiquitin-like proteins in the DNA damage response: the next generation. Essays Biochem. 64, 737–752 (2020).

    Google Scholar 

  141. Uzunova, K. et al. Ubiquitin-dependent proteolytic control of SUMO conjugates. J. Biol. Chem. 282, 34167–34175 (2007).

    Google Scholar 

  142. Cuijpers, S. A. G., Willemstein, E. & Vertegaal, A. C. O. Converging small ubiquitin-like modifier (SUMO) and ubiquitin signaling: improved methodology identifies co-modified target proteins. Mol. Cell Proteom. 16, 2281–2295 (2017).

    Google Scholar 

  143. Pichler, A. et al. SUMO modification of the ubiquitin-conjugating enzyme E2-25K. Nat. Struct. Mol. Biol. 12, 264–269 (2005).

    Google Scholar 

  144. Ranieri, M. et al. Sumoylation and ubiquitylation crosstalk in the control of ΔNp63α protein stability. Gene 645, 34–40 (2018).

    Google Scholar 

  145. Lecona, E. et al. USP7 is a SUMO deubiquitinase essential for DNA replication. Nat. Struct. Mol. Biol. 23, 270–277 (2016).

    Google Scholar 

  146. Hendriks, I. A. & Vertegaal, A. C. A comprehensive compilation of SUMO proteomics. Nat. Rev. Mol. Cell Biol. 17, 581–595 (2016).

    Google Scholar 

  147. Prudden, J. et al. SUMO-targeted ubiquitin ligases in genome stability. EMBO J. 26, 4089–4101 (2007).

    Google Scholar 

  148. Sun, H., Leverson, J. D. & Hunter, T. Conserved function of RNF4 family proteins in eukaryotes: targeting a ubiquitin ligase to SUMOylated proteins. EMBO J. 26, 4102–4112 (2007).

    Google Scholar 

  149. Poulsen, S. L. et al. RNF111/Arkadia is a SUMO-targeted ubiquitin ligase that facilitates the DNA damage response. J. Cell Biol. 201, 797–807 (2013).

    Google Scholar 

  150. Seenivasan, R. et al. Mechanism and chain specificity of RNF216/TRIAD3, the ubiquitin ligase mutated in Gordon Holmes syndrome. Hum. Mol. Genet. 28, 2862–2873 (2019).

    Google Scholar 

  151. Sriramachandran, A. M. et al. Arkadia/RNF111 is a SUMO-targeted ubiquitin ligase with preference for substrates marked with SUMO1-capped SUMO2/3 chain. Nat. Commun. 10, 3678 (2019).

    ADS  Google Scholar 

  152. Yin, Y. et al. SUMO-targeted ubiquitin E3 ligase RNF4 is required for the response of human cells to DNA damage. Genes Dev. 26, 1196–1208 (2012).

    Google Scholar 

  153. van Cuijk, L. et al. SUMO and ubiquitin-dependent XPC exchange drives nucleotide excision repair. Nat. Commun. 6, 7499 (2015).

    ADS  Google Scholar 

  154. Enchev, R. I., Schulman, B. A. & Peter, M. Protein neddylation: beyond Cullin–RING ligases. Nat. Rev. Mol. Cell Biol. 16, 30–44 (2015).

    Google Scholar 

  155. Hicke, L., Schubert, H. L. & Hill, C. P. Ubiquitin-binding domains. Nat. Rev. Mol. Cell Biol. 6, 610–621 (2005).

    Google Scholar 

  156. Husnjak, K. & Dikic, I. Ubiquitin-binding proteins: decoders of ubiquitin-mediated cellular functions. Annu. Rev. Biochem. 81, 291–322 (2012).

    Google Scholar 

  157. Aksnes, H., Ree, R. & Arnesen, T. Co-translational, post-translational, and non-catalytic roles of N-terminal acetyltransferases. Mol. Cell 73, 1097–1114 (2019).

    Google Scholar 

  158. Bloom, J., Amador, V., Bartolini, F., DeMartino, G. & Pagano, M. Proteasome-mediated degradation of p21 via N-terminal ubiquitinylation. Cell 115, 71–82 (2003).

    Google Scholar 

  159. Choe, K. N. & Moldovan, G. L. Forging ahead through darkness: PCNA, still the principal conductor at the replication fork. Mol. Cell 65, 380–392 (2017).

    Google Scholar 

  160. Weisshaar, S. R. et al. Arsenic trioxide stimulates SUMO-2/3 modification leading to RNF4-dependent proteolytic targeting of PML. FEBS Lett. 582, 3174–3178 (2008).

    Google Scholar 

  161. Lee, H. S., Lim, Y. S., Park, E. M., Baek, S. H. & Hwang, S. B. SUMOylation of nonstructural 5A protein regulates hepatitis C virus replication. J. Viral Hepat. 21, e108–e117 (2014).

    Google Scholar 

  162. McManus, F. P. et al. Quantitative SUMO proteomics reveals the modulation of several PML nuclear body associated proteins and an anti-senescence function of UBC9. Sci. Rep. 8, 7754 (2018).

    ADS  Google Scholar 

  163. Takeuchi, T., Iwahara, S., Saeki, Y., Sasajima, H. & Yokosawa, H. Link between the ubiquitin conjugation system and the ISG15 conjugation system: ISG15 conjugation to the UbcH6 ubiquitin E2 enzyme. J. Biochem. 138, 711–719 (2005).

    Google Scholar 

  164. Takeuchi, T. & Yokosawa, H. ISG15 modification of Ubc13 suppresses its ubiquitin-conjugating activity. Biochem. Biophys. Res. Commun. 336, 9–13 (2005).

    Google Scholar 

  165. Bialas, J., Groettrup, M. & Aichem, A. Conjugation of the ubiquitin activating enzyme UBE1 with the ubiquitin-like modifier FAT10 targets it for proteasomal degradation. PLoS ONE 10, e0120329 (2015).

    Google Scholar 

  166. Mukhopadhyay, D. & Riezman, H. Proteasome-independent functions of ubiquitin in endocytosis and signaling. Science 315, 201–205 (2007).

    ADS  Google Scholar 

  167. Kliza, K. et al. Internally tagged ubiquitin: a tool to identify linear polyubiquitin-modified proteins by mass spectrometry. Nat. Methods 14, 504–512 (2017).

    Google Scholar 

  168. Michel, M. A., Swatek, K. N., Hospenthal, M. K. & Komander, D. Ubiquitin linkage-specific affimers reveal insights into K6-linked ubiquitin signaling. Mol. Cell 68, 233–246.e5 (2017).

    Google Scholar 

  169. Ordureau, A. et al. Global landscape and dynamics of Parkin and USP30-dependent ubiquitylomes in ineurons during mitophagic signaling. Mol. Cell 77, 1124–1142.e10 (2020).

    Google Scholar 

  170. Yau, R. G. et al. Assembly and function of heterotypic ubiquitin chains in cell-cycle and protein quality control. Cell 171, 918–933 e920 (2017). This paper describes how complex mixed types of ubiquitin polymers play a role in cell cycle progression and protein quality control.

    Google Scholar 

  171. Hendriks, I. A., Schimmel, J., Eifler, K., Olsen, J. V. & Vertegaal, A. C. Ubiquitin-specific protease 11 (USP11) deubiquitinates hybrid small ubiquitin-like modifier (SUMO)-ubiquitin chains to counteract RING finger protein 4 (RNF4). J. Biol. Chem. 290, 15526–15537 (2015).

    Google Scholar 

  172. Guzzo, C. M. et al. RNF4-dependent hybrid SUMO-ubiquitin chains are signals for RAP80 and thereby mediate the recruitment of BRCA1 to sites of DNA damage. Sci. Signal. 5, ra88 (2012).

    Google Scholar 

  173. Maghames, C. M. et al. NEDDylation promotes nuclear protein aggregation and protects the ubiquitin proteasome system upon proteotoxic stress. Nat. Commun. 9, 4376 (2018).

    ADS  Google Scholar 

  174. Leidecker, O., Matic, I., Mahata, B., Pion, E. & Xirodimas, D. P. The ubiquitin E1 enzyme Ube1 mediates NEDD8 activation under diverse stress conditions. Cell Cycle 11, 1142–1150 (2012).

    Google Scholar 

  175. Skaug, B. & Chen, Z. J. Emerging role of ISG15 in antiviral immunity. Cell 143, 187–190 (2010).

    Google Scholar 

  176. Fan, J. B. et al. Identification and characterization of a novel ISG15–ubiquitin mixed chain and its role in regulating protein homeostasis. Sci. Rep. 5, 12704 (2015).

    ADS  Google Scholar 

  177. Zhao, C., Denison, C., Huibregtse, J. M., Gygi, S. & Krug, R. M. Human ISG15 conjugation targets both IFN-induced and constitutively expressed proteins functioning in diverse cellular pathways. Proc. Natl Acad. Sci. USA 102, 10200–10205 (2005).

    ADS  Google Scholar 

  178. Anania, V. G. et al. Peptide level immunoaffinity enrichment enhances ubiquitination site identification on individual proteins. Mol. Cell Proteom. 13, 145–156 (2014).

    Google Scholar 

  179. Phu, L. et al. Dynamic regulation of mitochondrial import by the ubiquitin system. Mol. Cell 77, 1107–1123.e10 (2020).

    Google Scholar 

  180. Huang, E. Y. et al. A VCP inhibitor substrate trapping approach (VISTA) enables proteomic profiling of endogenous ERAD substrates. Mol. Biol. Cell 29, 1021–1030 (2018).

    Google Scholar 

  181. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    Google Scholar 

  182. Wang, M. et al. Assembling the community-scale discoverable human proteome. Cell Syst. 7, 412–421.e5 (2018).

    Google Scholar 

  183. Okuda, S. et al. jPOSTrepo: an international standard data repository for proteomes. Nucleic Acids Res. 45, D1107–D1111 (2017).

    Google Scholar 

  184. Ma, J. et al. iProX: an integrated proteome resource. Nucleic Acids Res. 47, D1211–D1217 (2019).

    Google Scholar 

  185. Deutsch, E. W., Lam, H. & Aebersold, R. PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429–434 (2008).

    Google Scholar 

  186. Sharma, V. et al. Panorama public: a public repository for quantitative data sets processed in skyline. Mol. Cell Proteom. 17, 1239–1244 (2018).

    Google Scholar 

  187. Jentsch, S. & Psakhye, I. Control of nuclear activities by substrate-selective and protein-group SUMOylation. Annu. Rev. Genet. 47, 167–186 (2013).

    Google Scholar 

  188. Hewings, D. S., Flygare, J. A., Bogyo, M. & Wertz, I. E. Activity-based probes for the ubiquitin conjugation–deconjugation machinery: new chemistries, new tools, and new insights. FEBS J. 284, 1555–1576 (2017).

    Google Scholar 

  189. Ovaa, H. & Vertegaal, A. C. O. Probing ubiquitin and SUMO conjugation and deconjugation. Biochem. Soc. Trans. 46, 423–436 (2018).

    Google Scholar 

  190. Gui, W. et al. Cell-permeable activity-based ubiquitin probes enable intracellular profiling of human deubiquitinases. J. Am. Chem. Soc. 140, 12424–12433 (2018).

    Google Scholar 

  191. Swatek, K. N. & Komander, D. Ubiquitin modifications. Cell Res. 26, 399–422 (2016).

    Google Scholar 

  192. Gomes, F. et al. Top-down analysis of novel synthetic branched proteins. J. Mass. Spectrom. 54, 19–25 (2019).

    ADS  Google Scholar 

  193. Olsen, J. V. & Mann, M. Status of large-scale analysis of post-translational modifications by mass spectrometry. Mol. Cell Proteom. 12, 3444–3452 (2013).

    Google Scholar 

  194. Nielsen, M. L. et al. Iodoacetamide-induced artifact mimics ubiquitination in mass spectrometry. Nat. Methods 5, 459–460 (2008).

    Google Scholar 

  195. Udeshi, N. D. et al. Methods for quantification of in vivo changes in protein ubiquitination following proteasome and deubiquitinase inhibition. Mol. Cell Proteom. 11, 148–159 (2012).

    Google Scholar 

  196. Humphrey, S. J., Karayel, O., James, D. E. & Mann, M. High-throughput and high-sensitivity phosphoproteomics with the EasyPhos platform. Nat. Protoc. 13, 1897–1916 (2018).

    Google Scholar 

  197. Tran, J. C. et al. Mapping intact protein isoforms in discovery mode using top-down proteomics. Nature 480, 254–258 (2011).

    ADS  Google Scholar 

  198. Lee, A. E. et al. Preparing to read the ubiquitin code: top-down analysis of unanchored ubiquitin tetramers. J. Mass. Spectrom. 51, 629–637 (2016).

    ADS  Google Scholar 

  199. Mattern, M., Sutherland, J., Kadimisetty, K., Barrio, R. & Rodriguez, M. S. Using ubiquitin binders to decipher the ubiquitin code. Trends Biochem. Sci. 44, 599–615 (2019).

    Google Scholar 

  200. Huang, X. & Dixit, V. M. Drugging the undruggables: exploring the ubiquitin system for drug development. Cell Res. 26, 484–498 (2016).

    Google Scholar 

  201. Verma, R., Mohl, D. & Deshaies, R. J. Harnessing the power of proteolysis for targeted protein inactivation. Mol. Cell 77, 446–460 (2020).

    Google Scholar 

  202. Karayel, O., Michaelis, A. C., Mann, M., Schulman, B. A. & Langlois, C. R. DIA-based systems biology approach unveils E3 ubiquitin ligase-dependent responses to a metabolic shift. Proc. Natl Acad. Sci. USA 117, 32806–32815 (2020).

    Google Scholar 

  203. Mark, K. G., Loveless, T. B. & Toczyski, D. P. Isolation of ubiquitinated substrates by tandem affinity purification of E3 ligase-polyubiquitin-binding domain fusions (ligase traps). Nat. Protoc. 11, 291–301 (2016).

    Google Scholar 

  204. Kliza, K. & Husnjak, K. Resolving the complexity of ubiquitin networks. Front. Mol. Biosci. 7, 21 (2020).

    Google Scholar 

  205. Li, W. et al. Genome-wide and functional annotation of human E3 ubiquitin ligases identifies MULAN, a mitochondrial E3 that regulates the organelle’s dynamics and signaling. PLoS ONE 3, e1487 (2008).

    ADS  Google Scholar 

  206. O’Connor, H. F. & Huibregtse, J. M. Enzyme–substrate relationships in the ubiquitin system: approaches for identifying substrates of ubiquitin ligases. Cell Mol. Life Sci. 74, 3363–3375 (2017).

    Google Scholar 

  207. Liebelt, F. et al. The poly-SUMO2/3 protease SENP6 enables assembly of the constitutive centromere-associated network by group deSUMOylation. Nat. Commun. 10, 3987 (2019).

    ADS  Google Scholar 

  208. Andaluz Aguilar, H., Iliuk, A. B., Chen, I. H. & Tao, W. A. Sequential phosphoproteomics and N-glycoproteomics of plasma-derived extracellular vesicles. Nat. Protoc. 15, 161–180 (2020).

    Google Scholar 

  209. Melo-Braga, M. N., Ibanez-Vea, M., Larsen, M. R. & Kulej, K. Comprehensive protocol to simultaneously study protein phosphorylation, acetylation, and N-linked sialylated glycosylation. Methods Mol. Biol. 1295, 275–292 (2015).

    Google Scholar 

  210. Zhang, Y., Wang, H. & Lu, H. Sequential selective enrichment of phosphopeptides and glycopeptides using amine-functionalized magnetic nanoparticles. Mol. Biosyst. 9, 492–500 (2013).

    Google Scholar 

  211. Young, N. L., Plazas-Mayorca, M. D. & Garcia, B. A. Systems-wide proteomic characterization of combinatorial post-translational modification patterns. Expert Rev. Proteom. 7, 79–92 (2010).

    Google Scholar 

  212. Manasanch, E. E. & Orlowski, R. Z. Proteasome inhibitors in cancer therapy. Nat. Rev. Clin. Oncol. 14, 417–433 (2017).

    Google Scholar 

  213. Harrigan, J. A., Jacq, X., Martin, N. M. & Jackson, S. P. Deubiquitylating enzymes and drug discovery: emerging opportunities. Nat. Rev. Drug Discov. 17, 57–78 (2018).

    Google Scholar 

  214. Soucy, T. A. et al. An inhibitor of NEDD8-activating enzyme as a new approach to treat cancer. Nature 458, 732–736 (2009).

    ADS  Google Scholar 

  215. Hyer, M. L. et al. A small-molecule inhibitor of the ubiquitin activating enzyme for cancer treatment. Nat. Med. 24, 186–193 (2018).

    Google Scholar 

  216. He, X. et al. Probing the roles of SUMOylation in cancer cell biology by using a selective SAE inhibitor. Nat. Chem. Biol. 13, 1164–1171 (2017).

    Google Scholar 

  217. Lan, B., Chai, S., Wang, P. & Wang, K. VCP/p97/Cdc48, a linking of protein homeostasis and cancer therapy. Curr. Mol. Med. 17, 608–618 (2017).

    Google Scholar 

  218. Ferguson, F. M. & Gray, N. S. Kinase inhibitors: the road ahead. Nat. Rev. Drug Discov. 17, 353–377 (2018).

    Google Scholar 

  219. Bekker-Jensen, D. B. et al. A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients. Mol. Cell Proteom. 19, 716–729 (2020).

    Google Scholar 

  220. Bache, N. et al. A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics. Mol. Cell Proteom. 17, 2284–2296 (2018).

    Google Scholar 

  221. Doll, S., Gnad, F. & Mann, M. The case for proteomics and phospho-proteomics in personalized cancer medicine. Proteom. Clin. Appl. 13, e1800113 (2019).

    Google Scholar 

  222. Meierhofer, D., Wang, X., Huang, L. & Kaiser, P. Quantitative analysis of global ubiquitination in HeLa cells by mass spectrometry. J. Proteome Res. 7, 4566–4576 (2008).

    Google Scholar 

  223. Li, C. et al. Quantitative SUMO proteomics identifies PIAS1 substrates involved in cell migration and motility. Nat. Commun. 11, 834 (2020).

    ADS  Google Scholar 

  224. Schimmel, J. et al. Uncovering SUMOylation dynamics during cell-cycle progression reveals FoxM1 as a key mitotic SUMO target protein. Mol. Cell 53, 1053–1066 (2014).

    Google Scholar 

  225. Schimmel, J. et al. The ubiquitin–proteasome system is a key component of the SUMO-2/3 cycle. Mol. Cell Proteom. 7, 2107–2122 (2008).

    Google Scholar 

  226. Tatham, M. H., Matic, I., Mann, M. & Hay, R. T. Comparative proteomic analysis identifies a role for SUMO in protein quality control. Sci. Signal. 4, rs4 (2011).

    Google Scholar 

  227. Vertegaal, A. C. et al. Distinct and overlapping sets of SUMO-1 and SUMO-2 target proteins revealed by quantitative proteomics. Mol. Cell Proteom. 5, 2298–2310 (2006).

    Google Scholar 

  228. Li, Z. et al. Functions and substrates of NEDDylation during cell cycle in the silkworm, Bombyx mori. Insect Biochem. Mol. Biol. 90, 101–112 (2017).

    Google Scholar 

  229. Liao, S., Hu, H., Wang, T., Tu, X. & Li, Z. The protein neddylation pathway in Trypanosoma brucei: functional characterization and substrate identification. J. Biol. Chem. 292, 1081–1091 (2017).

    Google Scholar 

  230. Bennett, E. J., Rush, J., Gygi, S. P. & Harper, J. W. Dynamics of Cullin–RING ubiquitin ligase network revealed by systematic quantitative proteomics. Cell 143, 951–965 (2010).

    Google Scholar 

  231. Xirodimas, D. P. et al. Ribosomal proteins are targets for the NEDD8 pathway. EMBO Rep. 9, 280–286 (2008).

    Google Scholar 

  232. Jones, J. et al. A targeted proteomic analysis of the ubiquitin-like modifier NEDD8 and associated proteins. J. Proteome Res. 7, 1274–1287 (2008).

    ADS  Google Scholar 

  233. Hemelaar, J. et al. Specific and covalent targeting of conjugating and deconjugating enzymes of ubiquitin-like proteins. Mol. Cell Biol. 24, 84–95 (2004).

    Google Scholar 

  234. O’Connor, H. F., Swaim, C. D., Canadeo, L. A. & Huibregtse, J. M. Ubiquitin-activated interaction traps (UBAITs): tools for capturing protein–protein interactions. Methods Mol. Biol. 1844, 85–100 (2018).

    Google Scholar 

  235. Shi, Y. et al. A data set of human endogenous protein ubiquitination sites. Mol. Cell Proteom. 10, M110 002089 (2011).

    Google Scholar 

  236. Lopitz-Otsoa, F. et al. SUMO-binding entities (SUBEs) as tools for the enrichment, isolation, identification, and characterization of the SUMO proteome in liver cancer. J. Vis. Exp. https://doi.org/10.3791/60098 (2019).

    Article  Google Scholar 

  237. Bruderer, R. et al. Purification and identification of endogenous polySUMO conjugates. EMBO Rep. 12, 142–148 (2011).

    Google Scholar 

  238. Jeram, S. M. et al. An improved SUMmOn-based methodology for the identification of ubiquitin and ubiquitin-like protein conjugation sites identifies novel ubiquitin-like protein chain linkages. Proteomics 10, 254–265 (2010).

    Google Scholar 

  239. Baldanta, S. et al. ISG15 governs mitochondrial function in macrophages following vaccinia virus infection. PLoS Pathog. 13, e1006651 (2017).

    Google Scholar 

  240. Kozuka-Hata, H. et al. System-wide analysis of protein acetylation and ubiquitination reveals a diversified regulation in human cancer cells. Biomolecules 10, 411 (2020).

    Google Scholar 

  241. Akimov, V. et al. StUbEx PLUS — a modified stable tagged ubiquitin exchange system for peptide level purification and in-depth mapping of ubiquitination sites. J. Proteome Res. 17, 296–304 (2018).

    Google Scholar 

  242. Wagner, S. A. et al. A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles. Mol. Cell Proteom. 10, M111 013284 (2011).

    Google Scholar 

  243. Hendriks, I. A., D’Souza, R. C., Chang, J. G., Mann, M. & Vertegaal, A. C. System-wide identification of wild-type SUMO-2 conjugation sites. Nat. Commun. 6, 7289 (2015).

    ADS  Google Scholar 

  244. Cubenas-Potts, C. et al. Identification of SUMO-2/3-modified proteins associated with mitotic chromosomes. Proteomics 15, 763–772 (2015).

    Google Scholar 

  245. Becker, J. et al. Detecting endogenous SUMO targets in mammalian cells and tissues. Nat. Struct. Mol. Biol. 20, 525–531 (2013).

    Google Scholar 

  246. Matafora, V., D’Amato, A., Mori, S., Blasi, F. & Bachi, A. Proteomics analysis of nucleolar SUMO-1 target proteins upon proteasome inhibition. Mol. Cell Proteom. 8, 2243–2255 (2009).

    Google Scholar 

  247. Giannakopoulos, N. V. et al. Proteomic identification of proteins conjugated to ISG15 in mouse and human cells. Biochem. Biophys. Res. Commun. 336, 496–506 (2005).

    Google Scholar 

  248. Gupta, R. et al. Ubiquitination screen using protein microarrays for comprehensive identification of Rsp5 substrates in yeast. Mol. Syst. Biol. 3, 116 (2007).

    Google Scholar 

Download references

Acknowledgements

This work was carried out with financial support from the Natural Sciences and Engineering Research Council (NSERC 311598). IRIC proteomics facility is a Genomics Technology platform funded in part by the Canadian Government through Genome Canada, the Canadian Center of Excellence in Commercialization and Research, and the Canadian Foundation for Innovation. A.C.O.V. is supported by the European Research Council, the Dutch Research Council (NWO) and the Dutch Cancer Society.

Author information

Authors and Affiliations

Authors

Contributions

Introduction (P.T.); Experimentation (C.L., T.G.N., P.T. and A.C.O.V.); Results (A.C.O.V.); Applications (P.T.); Reproducibility and data deposition (A.C.O.V.); Limitations and optimizations (A.C.O.V.); Outlook (A.C.O.V.); Figs 1, 2, 3 and 5 (C.L.); Figs 4 and 7 and Box 1 and 2 (C.L. and T.G.N.); Table 1 (C.L., T.G.N. and P.T.); Overview of the Primer (A.C.O.V. and P.T.). All authors reviewed and edited the final manuscript.

Corresponding authors

Correspondence to Alfred C. O. Vertegaal or Pierre Thibault.

Ethics declarations

Competing interests

All authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Methods Primers thanks A. Ordureau, J. Peng, A. Pinto-Fernandez, G. Vere and the 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

Biorender: https://biorender.com/

iProX: https://www.iprox.org/

Japan ProteOme Standard Repository/Database: https://jpostdb.org/

JASSA: http://www.jassa.fr

Mass Spectrometry Interactive Virtual Environment (MassIVE): https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp

Panorama Public: https://panoramaweb.org

PeptideAtlas: http://www.peptideatlas.org/

PhosphoSitePlus: https://www.phosphosite.org

PRoteomics IDEntifications Database (PRIDE): https://www.ebi.ac.uk/pride/

UniProt: https://www.uniprot.org

Glossary

Ubiquitin code

The concept that distinct conformations of ubiquitin chains and modifications lead to different cellular outcomes.

De-NEDDylation

Removal of NEDD8 from modified substrates.

Isopycnic centrifugation

A fractionation method where cell components can be separated based on density gradient centrifugation.

Remnant peptides

Amino acids left over on modified lysine residues after proteolytic digestion.

Ubiquitylome

The cell-wide repertoire of ubiquitylated proteins.

Offline fractionation

Fractionation of peptide extracts by methods such as ion exchange or high-pH, reverse-phase chromatography; fractions are subsequently analysed by mass spectrometry.

Head-to-tail concatemers

Long, continuous DNA molecules containing multiple copies of the same gene assembled head to tail.

UBL traps

Affinity purification methods where fusion proteins containing units of ubiquitin-like protein (UBL)-binding domains are expressed in cells to trap UBL conjugates.

Ubiquitin clipping

A technique that uses an engineered viral protease, Lbpro, to cleave ubiquitin conjugates and leave a traceable diglycine remnant on the modified substrates.

Pulse chase

An experiment where cells are exposed to a labelled compound that is incorporated into proteins, which is later replaced with an unlabelled form to determine the time of exchange.

Ratio compression

Underestimation of the fold-change ratio of peptide/protein abundance in isobaric peptide labelling, owing to co-selection of different peptides during tandem mass spectrometry (MS/MS).

Translesion synthesis

A process whereby the DNA replication machinery can bypass the blocked replication fork caused by DNA damage.

Affimers

Non-antibody binding proteins that mimic the molecular recognition features of antibodies.

Top-down proteomics

A protein identification method that relies on the selection of protein ions as precursors for tandem mass spectrometry (MS/MS) fragmentation.

Proteolysis-targeting chimaeras

(PROTACs). Heterobifunctional small molecules that consist of a ubiquitin E3 ligase-binding domain linked to a domain that bind specifically to a protein targeted for degradation.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Nelson, T.G., Vertegaal, A.C.O. et al. Proteomic strategies for characterizing ubiquitin-like modifications. Nat Rev Methods Primers 1, 53 (2021). https://doi.org/10.1038/s43586-021-00048-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s43586-021-00048-9

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