Abstract
Targeted degradomics integrates positional information into mass spectrometry (MS)-based targeted proteomics workflows and thereby enables analysis of proteolytic cleavage events with unprecedented specificity and sensitivity. Rapid progress in the establishment of protease-substrate relations provides extensive degradomics target lists that now can be tested with help of selected and parallel reaction monitoring (S/PRM) in complex biological systems, where proteases act in physiological environments. In this minireview, we describe the general principles of targeted degradomics, outline the generic experimental workflow of the methodology and highlight recent and future applications in protease research.
Acknowledgments
We thank S. Werner (ETH Zurich, Switzerland) for her continuous support of our work and the Swiss National Science Foundation for funding with help of a project grant (31003A_163216).
References
Agard, N.J., Mahrus, S., Trinidad, J.C., Lynn, A., Burlingame, A.L., and Wells, J.A. (2012). Global kinetic analysis of proteolysis via quantitative targeted proteomics. Proc. Natl. Acad. Sci. USA 109, 1913–1918.10.1073/pnas.1117158109Search in Google Scholar PubMed PubMed Central
Anderson, N.L., Ptolemy, A.S., and Rifai, N. (2013). The riddle of protein diagnostics: future bleak or bright? Clin. Chem. 59, 194–197.10.1373/clinchem.2012.184705Search in Google Scholar PubMed
auf dem Keller, U. and Schilling, O. (2010). Proteomic techniques and activity-based probes for the system-wide study of proteolysis. Biochimie 92, 1705–1714.10.1016/j.biochi.2010.04.027Search in Google Scholar PubMed
Bourmaud, A., Gallien, S., and Domon, B. (2016). Parallel reaction monitoring using quadrupole-orbitrap mass spectrometer: principle and applications. Proteomics 16, 2146–2159.10.1002/pmic.201500543Search in Google Scholar PubMed
Choi, M., Chang, C.Y., Clough, T., Broudy, D., Killeen, T., MacLean, B., and Vitek, O. (2014). MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524–2526.10.1093/bioinformatics/btu305Search in Google Scholar PubMed
Crawford, E.D., Seaman, J.E., Agard, N., Hsu, G.W., Julien, O., Mahrus, S., Nguyen, H., Shimbo, K., Yoshihara, H.A., Zhuang, M., et al. (2013). The DegraBase: a database of proteolysis in healthy and apoptotic human cells. Mol. Cell. Proteomics 12, 813–824.10.1074/mcp.O112.024372Search in Google Scholar PubMed PubMed Central
Di Palma, S., Hennrich, M.L., Heck, A.J., and Mohammed, S. (2012). Recent advances in peptide separation by multidimensional liquid chromatography for proteome analysis. J. Proteomics 75, 3791–3813.10.1016/j.jprot.2012.04.033Search in Google Scholar PubMed
Dix, M.M., Simon, G.M., Wang, C., Okerberg, E., Patricelli, M.P., and Cravatt, B.F. (2012). Functional interplay between caspase cleavage and phosphorylation sculpts the apoptotic proteome. Cell 150, 426–440.10.1016/j.cell.2012.05.040Search in Google Scholar PubMed PubMed Central
Drag, M. and Salvesen, G.S. (2010). Emerging principles in protease-based drug discovery. Nat. Rev. Drug Discov. 9, 690–701.10.1038/nrd3053Search in Google Scholar PubMed PubMed Central
Dutta, A., Potier, D.N., Walker, M.J., Gray, O.J., Parker, C., Holland, M., Williamson, A.J., Pierce, A., Unwin, R.D., Krishnan, S., et al. (2016). Development of a selected reaction monitoring mass spectrometry-based assay to detect asparaginyl endopeptidase activity in biological fluids. Oncotarget 7, 70822–70831.10.18632/oncotarget.12224Search in Google Scholar PubMed PubMed Central
Ebhardt, H.A., Root, A., Sander, C., and Aebersold, R. (2015). Applications of targeted proteomics in systems biology and translational medicine. Proteomics 15, 3193–3208.10.1002/pmic.201500004Search in Google Scholar PubMed PubMed Central
Eichelbaum, K., Winter, M., Berriel Diaz, M., Herzig, S., and Krijgsveld, J. (2012). Selective enrichment of newly synthesized proteins for quantitative secretome analysis. Nat. Biotechnol. 30, 984–990.10.1038/nbt.2356Search in Google Scholar PubMed
Fahlman, R.P., Chen, W., and Overall, C.M. (2014). Absolute proteomic quantification of the activity state of proteases and proteolytic cleavages using proteolytic signature peptides and isobaric tags. J. Proteomics 100, 79–91.10.1016/j.jprot.2013.09.006Search in Google Scholar PubMed
Fortelny, N., Cox, J.H., Kappelhoff, R., Starr, A.E., Lange, P.F., Pavlidis, P., and Overall, C.M. (2014). Network analyses reveal pervasive functional regulation between proteases in the human protease web. PLoS Biol. 12, e1001869.10.1371/journal.pbio.1001869Search in Google Scholar PubMed PubMed Central
Fortelny, N., Yang, S., Pavlidis, P., Lange, P.F., and Overall, C.M. (2015). Proteome TopFIND 3.0 with TopFINDer and PathFINDer: database and analysis tools for the association of protein termini to pre- and post-translational events. Nucleic Acids Res. 43, D290–D297.10.1093/nar/gku1012Search in Google Scholar PubMed PubMed Central
Gallien, S., Kim, S.Y., and Domon, B. (2015). Large-scale targeted proteomics using internal standard triggered-parallel reaction monitoring (IS-PRM). Mol. Cell. Proteomics 14, 1630–1644.10.1074/mcp.O114.043968Search in Google Scholar PubMed PubMed Central
Giansanti, P., Tsiatsiani, L., Low, T.Y., and Heck, A.J. (2016). Six alternative proteases for mass spectrometry-based proteomics beyond trypsin. Nat. Protoc. 11, 993–1006.10.1038/nprot.2016.057Search in Google Scholar PubMed
Goettig, P. (2016). Effects of glycosylation on the enzymatic activity and mechanisms of proteases. Int. J. Mol. Sci. 17, E1969.10.3390/ijms17121969Search in Google Scholar PubMed PubMed Central
Hu, S., Loo, J.A., and Wong, D.T. (2006). Human body fluid proteome analysis. Proteomics 6, 6326–6353.10.1002/pmic.200600284Search in Google Scholar PubMed PubMed Central
Huesgen, P.F., Lange, P.F., and Overall, C.M. (2014). Ensembles of protein termini and specific proteolytic signatures as candidate biomarkers of disease. Proteomics Clin. Appl. 8, 338–350.10.1002/prca.201300104Search in Google Scholar PubMed
Huesgen, P.F., Lange, P.F., Rogers, L.D., Solis, N., Eckhard, U., Kleifeld, O., Goulas, T., Gomis-Ruth, F.X., and Overall, C.M. (2015). LysargiNase mirrors trypsin for protein C-terminal and methylation-site identification. Nat. Methods 12, 55–58.10.1038/nmeth.3177Search in Google Scholar PubMed
Igarashi, Y., Eroshkin, A., Gramatikova, S., Gramatikoff, K., Zhang, Y., Smith, J.W., Osterman, A.L., and Godzik, A. (2007). CutDB: a proteolytic event database. Nucleic Acids Res. 35, D546–D549.10.1093/nar/gkl813Search in Google Scholar PubMed PubMed Central
Julien, O., Zhuang, M., Wiita, A.P., O‘Donoghue, A.J., Knudsen, G.M., Craik, C.S., and Wells, J.A. (2016). Quantitative MS-based enzymology of caspases reveals distinct protein substrate specificities, hierarchies, and cellular roles. Proc. Natl. Acad. Sci. USA 113, E2001–E2010.10.1073/pnas.1524900113Search in Google Scholar PubMed PubMed Central
Kockmann, T., Trachsel, C., Panse, C., Wahlander, A., Selevsek, N., Grossmann, J., Wolski, W.E., and Schlapbach, R. (2016). Targeted proteomics coming of age - SRM, PRM and DIA performance evaluated from a core facility perspective. Proteomics 16, 2183–2192.10.1002/pmic.201500502Search in Google Scholar PubMed
Kumar, S., van Raam, B.J., Salvesen, G.S., and Cieplak, P. (2014). Caspase cleavage sites in the human proteome: CaspDB, a database of predicted substrates. PLoS One 9, e110539.10.1371/journal.pone.0110539Search in Google Scholar PubMed PubMed Central
Kusebauch, U., Campbell, D.S., Deutsch, E.W., Chu, C.S., Spicer, D.A., Brusniak, M.Y., Slagel, J., Sun, Z., Stevens, J., Grimes, B., et al. (2016). Human SRMAtlas: a resource of targeted assays to quantify the complete human proteome. Cell 166, 766–778.10.1016/j.cell.2016.06.041Search in Google Scholar PubMed PubMed Central
MacLean, B., Tomazela, D.M., Shulman, N., Chambers, M., Finney, G.L., Frewen, B., Kern, R., Tabb, D.L., Liebler, D.C., and MacCoss, M.J. (2010). Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968.10.1093/bioinformatics/btq054Search in Google Scholar PubMed PubMed Central
Marino, G., Eckhard, U., and Overall, C.M. (2015). Protein termini and their modifications revealed by positional proteomics. ACS Chem. Biol. 10, 1754–1764.10.1021/acschembio.5b00189Search in Google Scholar PubMed
Navarro, P., Kuharev, J., Gillet, L.C., Bernhardt, O.M., MacLean, B., Rost, H.L., Tate, S.A., Tsou, C.C., Reiter, L., Distler, U., et al. (2016). A multicenter study benchmarks software tools for label-free proteome quantification. Nat. Biotechnol. 34, 1130–1136.10.1038/nbt.3685Search in Google Scholar PubMed PubMed Central
Picotti, P. and Aebersold, R. (2012). Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat. Methods 9, 555–566.10.1038/nmeth.2015Search in Google Scholar PubMed
Poli, M., Ori, A., Child, T., Jaroudi, S., Spath, K., Beck, M., and Wells, D. (2015). Characterization and quantification of proteins secreted by single human embryos prior to implantation. EMBO Mol. Med. 7, 1465–1479.10.15252/emmm.201505344Search in Google Scholar PubMed PubMed Central
Rawlings, N.D., Barrett, A.J., and Finn, R. (2016). Twenty years of the MEROPS database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res. 44, D343–D350.10.1093/nar/gkv1118Search in Google Scholar PubMed PubMed Central
Rogers, L.D. and Overall, C.M. (2013). Proteolytic post-translational modification of proteins: proteomic tools and methodology. Mol. Cell. Proteomics 12, 3532–3542.10.1074/mcp.M113.031310Search in Google Scholar PubMed PubMed Central
Rost, H.L., Malmstrom, L., and Aebersold, R. (2015). Reproducible quantitative proteotype data matrices for systems biology. Mol. Biol. Cell 26, 3926–3931.10.1091/mbc.E15-07-0507Search in Google Scholar PubMed PubMed Central
Sabino, F., Hermes, O., Egli, F.E., Kockmann, T., Schlage, P., Croizat, P., Kizhakkedathu, J.N., Smola, H., and auf dem Keller, U. (2015). In vivo assessment of protease dynamics in cutaneous wound healing by degradomics analysis of porcine wound exudates. Mol. Cell. Proteomics 14, 354–370.10.1074/mcp.M114.043414Search in Google Scholar PubMed PubMed Central
Schlage, P. and auf dem Keller, U. (2015). Proteomic approaches to uncover MMP function. Matrix Biol. 44–46C, 232–238.10.1016/j.matbio.2015.01.003Search in Google Scholar PubMed
Shimbo, K., Hsu, G.W., Nguyen, H., Mahrus, S., Trinidad, J.C., Burlingame, A.L., and Wells, J.A. (2012). Quantitative profiling of caspase-cleaved substrates reveals different drug-induced and cell-type patterns in apoptosis. Proc. Natl. Acad. Sci. USA 109, 12432–12437.10.1073/pnas.1208616109Search in Google Scholar PubMed PubMed Central
Song, J., Tan, H., Boyd, S.E., Shen, H., Mahmood, K., Webb, G.I., Akutsu, T., Whisstock, J.C., and Pike, R.N. (2011). Bioinformatic approaches for predicting substrates of proteases. J. Bioinform. Comput. Biol. 9, 149–178.10.1142/S0219720011005288Search in Google Scholar
Soste, M., Hrabakova, R., Wanka, S., Melnik, A., Boersema, P., Maiolica, A., Wernas, T., Tognetti, M., von Mering, C., and Picotti, P. (2014). A sentinel protein assay for simultaneously quantifying cellular processes. Nat. Methods 11, 1045–1048.10.1038/nmeth.3101Search in Google Scholar PubMed
Streng, A.S., de Boer, D., Bouwman, F.G., Mariman, E.C., Scholten, A., van Dieijen-Visser, M.P., and Wodzig, W.K. (2016). Development of a targeted selected ion monitoring assay for the elucidation of protease induced structural changes in cardiac troponin T. J. Proteomics 136, 123–132.10.1016/j.jprot.2015.12.028Search in Google Scholar PubMed
Tagliabracci, V.S., Engel, J.L., Wiley, S.E., Xiao, J., Gonzalez, D.J., Nidumanda Appaiah, H., Koller, A., Nizet, V., White, K.E., and Dixon, J.E. (2014). Dynamic regulation of FGF23 by Fam20C phosphorylation, GalNAc-T3 glycosylation, and furin proteolysis. Proc. Natl. Acad. Sci. USA 111, 5520–5525.10.1073/pnas.1402218111Search in Google Scholar PubMed PubMed Central
Turk, B., Turk, D.S.A., and Turk, V. (2012). Protease signalling: the cutting edge. EMBO J. 31, 1630–1643.10.1038/emboj.2012.42Search in Google Scholar PubMed PubMed Central
Turowec, J.P., Zukowski, S.A., Knight, J.D., Smalley, D.M., Graves, L.M., Johnson, G.L., Li, S.S., Lajoie, G.A., and Litchfield, D.W. (2014). An unbiased proteomic screen reveals caspase cleavage is positively and negatively regulated by substrate phosphorylation. Mol. Cell. Proteomics 13, 1184–1197.10.1074/mcp.M113.037374Search in Google Scholar PubMed PubMed Central
Wiita, A.P., Hsu, G.W., Lu, C.M., Esensten, J.H., and Wells, J.A. (2014). Circulating proteolytic signatures of chemotherapy-induced cell death in humans discovered by N-terminal labeling. Proc. Natl. Acad. Sci. USA 111, 7594–7599.10.1073/pnas.1405987111Search in Google Scholar PubMed PubMed Central
©2018 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Reviews
- Maintaining protein composition in cilia
- Eremophilane-type sesquiterpenes from fungi and their medicinal potential
- How to get rid of mitochondria: crosstalk and regulation of multiple mitophagy pathways
- Minireviews
- Targeted degradomics in protein terminomics and protease substrate discovery
- Brain plasticity, cognitive functions and neural stem cells: a pivotal role for the brain-specific neural master gene |-SRGAP2–FAM72-|
- Research Articles/Short Communications
- Protein Structure and Function
- Domain topology of human Rasal
- The consequences of deglycosylation of recombinant intra-melanosomal domain of human tyrosinase
- Cell Biology and Signaling
- Locally produced xenin and the neurotensinergic system in pancreatic islet function and β-cell survival
- The long non-coding RNA CRNDE promotes cervical cancer cell growth and metastasis