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The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage

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Abstract

Cytotoxic T cells (CTLs) perceive the world through small peptides that are eight to ten amino acids long. These peptides (epitopes) are initially generated by the proteasome, a multi-subunit protease that is responsible for the majority of intra-cellular protein degradation. The proteasome generates the exact C-terminal of CTL epitopes, and the N-terminal with a possible extension. CTL responses may diminish if the epitopes are destroyed by the proteasomes. Therefore, the prediction of the proteasome cleavage sites is important to identify potential immunogenic regions in the proteomes of pathogenic microorganisms (or humans). We have recently shown that NetChop, a neural network-based prediction method, is the best method available at the moment to do such predictions; however, its performance is still lower than desired. Here, we use novel sequence encoding methods and show that the new version of NetChop predicts approximately 10% more of the cleavage sites correctly while lowering the number of false positives with close to 15%. With this more reliable prediction tool, we study two important questions concerning the function of the proteasome. First, we estimate the N-terminal extension of epitopes after proteasomal cleavage and find that the average extension is relatively short. However, more than 30% of the peptides have N-terminal extensions of three amino acids or more, and thus, N-terminal trimming might play an important role in the presentation of a substantial fraction of the epitopes. Second, we show that good TAP ligands have an increased chance of being cleaved by the proteasome, i.e., the specificity of TAP has evolved to fit the specificity of the proteasome. This evolutionary relationship allows for a more efficient antigen presentation.

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References

  • Bairoch A, Apweiler R (2000) The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 28:45–48

    Article  CAS  PubMed  Google Scholar 

  • Baldi P, Brunak S (2001) Bioinformatics: the machine learning approach, 2nd edn. MIT Press, Cambridge

    Google Scholar 

  • Cascio P, Hilton C, Kisselev AF, Rock KL, Goldberg AL (2001) 26S Proteasomes and immunoproteasomes produce mainly N-extended versions of an antigenic peptide. EMBO J 20:2357–2366

    Google Scholar 

  • Eggers M, Boes-Fabian B, Ruppert T, Kloetzel PM, Koszinowski UH (1995) The cleavage preference of the proteasome governs the yield of antigenic peptides. J Exp Med 182:1865–1870

    Google Scholar 

  • Goldberg AL, Cascio P, Saric T, Rock KL (2002) The importance of the proteasome and subsequent proteolytic steps in the generation of antigenic peptides. Mol Immunol 39:147–164

    Google Scholar 

  • Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci USA 89:10915–10919

    CAS  PubMed  Google Scholar 

  • Holzhutter HG, Frommel C, Kloetzel PM (1999) A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20 S proteasome. J Mol Biol 286:1251–1265

    Google Scholar 

  • Kesmir C, Nussbaum AK, Schild H, Detours V, Brunak S (2002) Prediction of proteasome cleavage motifs by neural networks. Protein Eng 15:287–296

    Article  CAS  PubMed  Google Scholar 

  • Kesmir C, Noort VV, Boer RJD, Hogeweg P (2003) Bioinformatic analysis of functional differences between the immunoproteasome and the constitutive proteasome. Immunogenetics 55:437–449

    Article  CAS  PubMed  Google Scholar 

  • Kuttler C, Nussbaum AK, Dick TP, Rammensee HG, Schild H, Hadeler KP (2000) An algorithm for the prediction of proteasomal cleavages. J Mol Biol 298:417–429

    Article  CAS  PubMed  Google Scholar 

  • Levy F, Burri L, Morel S, Peitrequin AL, Levy N, Bachi A, Hellman U, Van den Eynde BJ, Servis C (2002) The final N-terminal trimming of a subaminoterminal proline-containing HLA class I-restricted antigenic peptide in the cytosol is mediated by two peptidases. J Immunol 169:4161–4171

    Google Scholar 

  • Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O (2003) Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 12:1007–1017

    Google Scholar 

  • Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K, Buus S, Brunak S, Lund O (2004) Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 20:1388–1397

    Google Scholar 

  • Nussbaum AK, Dick TP, Keilholz W, Schirle M, Stevanovic S, Dietz K, Heinemeyer W, Groll M, Wolf DH, Huber R, Rammensee HG, Schild H (1998) Cleavage motifs of the yeast 20S proteasome β subunits deduced from digests of enolase 1. Proc Natl Acad Sci USA 95:12504–12509

    Article  CAS  PubMed  Google Scholar 

  • Peters B, Bulik S, Tampe R, Endert PMV, Holzhutter HG (2003) Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. J Immunol 171:1741–1749

    CAS  PubMed  Google Scholar 

  • Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge

    Google Scholar 

  • Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50:213–219

    Article  CAS  PubMed  Google Scholar 

  • Reits E, Griekspoor A, Neijssen J, Groothuis T, Jalink K, Veelen PV, Janssen H, Calafat J, Drijfhout JW, Neefjes J (2003) Peptide diffusion, protection, and degradation in nuclear and cytoplasmic compartments before antigen presentation by MHC class I. Immunity 18:97–108

    Google Scholar 

  • Reits E, Neijssen J, Herberts C, Benckhuijsen W, Janssen L, Drijfhout JW, Neefjes J (2004) A major role for TPPII in trimming proteasomal degradation products for MHC class I antigen presentation. Immunity 20:495–506

    Article  CAS  PubMed  Google Scholar 

  • Saric T, Chang SC, Hattori A, York IA, Markant S, Rock KL, Tsujimoto M, Goldberg AL (2002) An IFN-gamma-induced aminopeptidase in the ER, ERAP1, trims precursors to MHC class I-presented peptides. Nat Immunol 3:1169–1176

    Google Scholar 

  • Saxová P, Buus S, Brunak S, Kesmir C (2003) Predicting proteasomal cleavage sites: a comparison of available methods. Int Immunol 15:781–787

    Article  CAS  PubMed  Google Scholar 

  • Schneider TD, Stephens RM (1990) Sequence logos: a new way to display consensus sequences. Nucleic Acids Res 18:6097–6100

    CAS  PubMed  Google Scholar 

  • Serwold T, Gonzalez F, Kim J, Jacob R, Shastri N (2002) ERAAP customizes peptides for MHC class I molecules in the endoplasmic reticulum. Nature 419:480–483

    Article  CAS  PubMed  Google Scholar 

  • Stoltze L, Nussbaum AK, Sijts A, Emmerich NP, Kloetzel PM, Schild H (2000) The function of the proteasome system in MHC class I antigen processing. Immunol Today 21:317–319

    Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    CAS  PubMed  Google Scholar 

  • Tenzer S, Stoltze L, Schonfisch B, Dengjel J, Muller M, Stevanovic S, Rammensee HG, Schild H (2004) Quantitative analysis of prion-protein degradation by constitutive and immuno-20S proteasomes indicates differences correlated with disease susceptibility. J Immunol 172:1083–1091

    Google Scholar 

  • Thorne JL, Goldman N, Jones DT (1996) Combining protein evolution and secondary structure. Mol Biol Evol 13:666–673

    Google Scholar 

  • Toes RE, Nussbaum AK, Degermann S, Schirle M, Emmerich NP, Kraft M, Laplace C, Zwinderman A, Dick TP, Muller J, Schonfisch B, Schmid C, Fehling HJ, Stevanovic S, Rammensee HG, Schild H (2001) Discrete cleavage motifs of constitutive and immunoproteasomes revealed by quantitative analysis of cleavage products. J Exp Med 194:1–12

    Article  CAS  PubMed  Google Scholar 

  • van Endert PM (1996) Peptide selection for presentation by HLA class I: a role for the human transporter associated with antigen processing? Immunol Res 15:265–279

    Google Scholar 

  • Yewdell JW (2001) Not such a dismal science: the economics of protein synthesis, folding, degradation and antigen processing. Trends Cell Biol 11:294–297

    Google Scholar 

  • York IA, Chang SC, Saric T, Keys JA, Favreau JM, Goldberg AL, Rock KL (2002) The ER aminopeptidase ERAP1 enhances or limits antigen presentation by trimming epitopes to 8–9 residues. Nat Immunol 3:1177–1184

    Google Scholar 

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Acknowledgements

This work was supported by the 5th Framework Programme of the European Commission (grant QLRT-1999-00173), the Netherlands Organization for Scientific Research (NWO, grant 050.50.202), and the NIH (grant AI49213-02).

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Correspondence to Morten Nielsen.

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The new version of NetChop (NetChop 3.0) is available at http://www.cbs.dtu.dk/services/NetChop-3.0.

Appendix A

Appendix A

The AROC values for the NetChop 20S and the NetChop 20S-3.0 are 0.81 and 0.85, respectively, and the CC and PCC are 0.41, 0.48 and 0.48, 0.55, respectively. To estimate the statistical significance of the difference in predictive performance between two methods, we performed the bootstrap experiment as described above. We found that the NetChop 20S–3.0 method has a performance that is significantly higher that that of NetChop 20S in terms of both the PCC, and the AROC values (P<0.05). The performance difference between the two methods in terms of the Mathews CC is not significant (P>0.3). That the performance increase is least significant in a fixed cut-off classification measure (Matthews CC) is not surprising, as the NetChop 20S method was trained to have an explicit classification bias around 0.5. This is not the case for any of the new neural networks.

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Nielsen, M., Lundegaard, C., Lund, O. et al. The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 57, 33–41 (2005). https://doi.org/10.1007/s00251-005-0781-7

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  • DOI: https://doi.org/10.1007/s00251-005-0781-7

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