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.
Similar content being viewed by others
References
Bairoch A, Apweiler R (2000) The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 28:45–48
Baldi P, Brunak S (2001) Bioinformatics: the machine learning approach, 2nd edn. MIT Press, Cambridge
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
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
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
Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci USA 89:10915–10919
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
Kesmir C, Nussbaum AK, Schild H, Detours V, Brunak S (2002) Prediction of proteasome cleavage motifs by neural networks. Protein Eng 15:287–296
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
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
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
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
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
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
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
Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipes in C: the art of scientific computing. Cambridge University Press, Cambridge
Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50:213–219
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
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
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
Saxová P, Buus S, Brunak S, Kesmir C (2003) Predicting proteasomal cleavage sites: a comparison of available methods. Int Immunol 15:781–787
Schneider TD, Stephens RM (1990) Sequence logos: a new way to display consensus sequences. Nucleic Acids Res 18:6097–6100
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
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
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293
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
Thorne JL, Goldman N, Jones DT (1996) Combining protein evolution and secondary structure. Mol Biol Evol 13:666–673
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
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
Yewdell JW (2001) Not such a dismal science: the economics of protein synthesis, folding, degradation and antigen processing. Trends Cell Biol 11:294–297
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
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).
Author information
Authors and Affiliations
Corresponding author
Additional information
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.
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00251-005-0781-7