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.

  • Research Article
  • Published:

Application of an artificial neural network to predict specific class I MHC binding peptide sequences

Nature Biotechnology volume 16, pages 753–756 (1998)Cite this article

Abstract

Computational methods were used to predict the sequences of peptides that bind to the MHC class I molecule, Kb. The rules for predicting binding sequences, which are limited, are based on preferences for certain amino acids in certain positions of the peptide. It is apparent though, that binding can be influenced by the amino acids in all of the positions of the peptide. An artificial neural network (ANN) has the ability to simultaneously analyze the influence of all of the amino acids of the peptide and thus may improve binding predictions. ANNs were compared to statistically analyzed peptides for their abilities to predict the sequences of Kb binding peptides. ANN systems were trained on a library of binding and non-binding peptide sequences from a phage display library. Statistical and ANN methods identified strong binding peptides with preferred amino acids. ANNs detected more subtle binding preferences, enabling them to predict medium binding peptides. The ability to predict class I MHC molecule binding peptides is useful for immunolological therapies involving cytotoxic-T cells.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

References

  1. Schneider, G., Schuchhardt, J. and Wrede, P. 1995. Peptide design in machina: development of artificial mitochondrial protein precursor cleavage sites by simulated molecular evolution. Biophysical J. 68: 434–447.

    Article  CAS  Google Scholar 

  2. Lohmann, R., Schneider, G., Behrens, D. and Wrede, P. 1994. A neural network model for the prediction of membrane-spanning amino acid sequences. Protein Sci. 9: 1597–1601.

    Article  Google Scholar 

  3. Milik, M. and Skolnick, J. 1995. An object oriented environment for artificial evolution of protein sequences: the example of rational design of transmembrane sequences, in Proceeding of Fourth Annual Conference on Evolutionary Programming. MIT Press, La Jolla.CA.

    Google Scholar 

  4. Townsend, A. and Bodmer, H. 1989. Antigen recognition by class l-restricted T lymphocytes. Annu. Rev. Immunol. 7: 601–624.

    Article  CAS  Google Scholar 

  5. Yewdell, J.W. and Bennink, J.R. 1992. Cell biology of antigen processing and presentation to major histocompatibility complex class I molecule-restricted T lymphocytes. Advances in Immunology 52: 1–123.

    Article  CAS  Google Scholar 

  6. Falk, K., Rotzschke, O., Stevanovic, S., Jung, G. and Rammensee, H.G. 1991. Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 351: 290–296.

    Article  CAS  Google Scholar 

  7. Jardetzky, T.S., Lane, W.S., Robinson, R.A., Madden, D.R. and Wiley, D.C. 1991. Identification of self peptides bound to purified HLA-B27. Nature 353: 326–329.

    Article  CAS  Google Scholar 

  8. Hunt, D.F., Henderson, R.A., Shabanowitz, J., Sakaguchi, K., Michel, H., Sevilir, N. et al. 1992. Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry. Science 255: 1261–1263.

    Article  CAS  Google Scholar 

  9. Ruppert, J., Sidney, J., Celis, E., Kubo, R.T., Grey, H.M. and Sette, A. 1993. Prominent role of secondary anchor residues in peptide binding to HLA-A2. 1 molecules. Cell 74: 929–937.

    Article  CAS  Google Scholar 

  10. Garboczi, D.N., Madden, D.R. and Wiley, D.C. 1994. Simultaneous space group determination and x-ray data collection. J. Mol. Blol. 239: 581–587.

    Article  CAS  Google Scholar 

  11. Matsumura, M., Saito, Y., Jackson, M.R., Song, E.S. and Peterson, P.A. 1992. In vitro peptide binding to soluble empty class I major histocompatibility complex molecules isolated from transfected Drosophila melanogaster cells. J. Blol. Chem. 267: 23589–23595.

    CAS  Google Scholar 

  12. Hammer, J., Takacs, B. and Sinigaglia, F. 1992. Identification of a motif for HLA-DR1 binding peptides using M13 display libraries. J. Exp. Med. 176: 1007–1013.

    Article  CAS  Google Scholar 

  13. Jackson, M.R., Song, E., Yang, Y. and Peterson, P.A. 1992. Empty and peptide-containing conformers of class I major histocompatibility complex molecules expressed in Drosophila melanogaster cells. Prac. Natl.Acad. Sci. USA 89: 12117–12121.

    Article  CAS  Google Scholar 

  14. Parmley, S.F. and Smith, G.P. 1988. Antibody-selectable filamentous fd phage vectors: affinity purification of target genes. Gene 73: 305–318.

    Article  CAS  Google Scholar 

  15. Scott, J.K. and Smith, G.P. 1990. Searching for peptide ligands with an epitope library. Science 249: 386–390.

    Article  CAS  Google Scholar 

  16. Rammensee, H.G., Friede, T. and Stevanoviic, S. 1995. MHC ligands and peptide motifs: first listing. Immunogenetics 41: 178–228.

    Article  CAS  Google Scholar 

  17. Taylor, W.R. 1986. The classification of amino acid conservation. J. Theor. Biol. 119: 205–218.

    Article  CAS  Google Scholar 

  18. Kidera, A., Konishi, Y., Oka, M., Ooi, T. and Sheraga, H.A. 1985. Statistical analysis of the physical properties of the 20 naturally occurring amino acids. J. Prof. Chem. 4: 23–55.

    Article  CAS  Google Scholar 

  19. Brusic, V., Rudy, R. and Harrison, L.C. 1994. Prediction of MHC binding peptides using artificial neural networks, pp. 253–260 in Complex systems: Mechanism of adaptation, Stonier, R.J. and Yu, X.S. (eds.). IOS Press, Washington, DC.

    Google Scholar 

  20. Adams, H.P. and Koziol, J.A. 1995. Prediction of binding to MHC class I molecules. J. Immunol. Methods 185: 181–190.

    Article  CAS  Google Scholar 

  21. Gulukota, K., Sidney, J., Sette, A. and Delisi, C. 1997. Two complementary methods for predicting peptides binding major histocompatibility complex molecules. J. Mol. Biol. 267: 1258–1267.

    Article  CAS  Google Scholar 

  22. von Heijne, G. 1986. Net N-C charge imbalance may be important for signal sequence function in bacteria. J. Mol. Biol. 192: 287–290.

    Article  CAS  Google Scholar 

  23. Mullis, K.B. and Faloona, F.A. 1987. Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Methods Enzymol. 155: 335–350.

    Article  CAS  Google Scholar 

  24. Field, J., Nikawa, J., Broek, D., MacDonald, B., Rodgers, L., Wilson, I.A. et al. 1988. Purification of a RAS-responsive adenylyl cyclase complex from Saccharomyces cerevisiae by use of an epitope addition method. Mol. Cell. Biol. 8: 2159–2165.

    Article  CAS  Google Scholar 

  25. Skerra, A., Pfitzinger, I. and Pluckthun, A. 1991. The functional expression of antibody Fv fragments in Escherichia coli: improved vectors and a generally applicable purification technique. Bio/Technology 9: 273–278.

    CAS  Google Scholar 

  26. Laemmli, U.K. 1970. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 227: 680–685.

    Article  CAS  Google Scholar 

  27. Smith, G.P. 1988. Filamentous phage assembly: morphogenetically defective mutants that do not kill the host. Virology 167: 156–165.

    Article  CAS  Google Scholar 

  28. Castano, A.R., Tangri, S., Miller, J.E. HoHcombe, H.R., Jackson, M.R., Huse, W.D. et al. 1995. Peptide binding and presentation by mouse CD1. Science 269: 223–226.

    Article  CAS  Google Scholar 

  29. Zell, A. and Marnier, G. G.1997. Stuttgart Neural Network Simulator version 4.1: University of Stuttgart.

  30. Rumelhart, D., McClelland, J. and PDP Research Group 1996. Parallel Distributed Processing. MIT Press, Cambridge, MA.

    Google Scholar 

  31. Masters, T. 1993. Recipes in C++. Academic Press Inc., Boston.

  32. Jameson, S.C. and Bevan, J. 1992. Dissection of major histocompatibility complex (MHC) and T cell receptor contact residues in a Kb-rstricted ovalbumin peptide and an assessment of the predictive power of MHC-binding motifs. Eur. J. Immunol. 22: 2663–2667.

    Article  CAS  Google Scholar 

  33. Karre, K., Ljunggren, H.G., Piontek, G. and Kiessling, R. 1986. Selective reaction of H-2 deficient lymphoma variants suggests alternative immune defense strategy. Nature 319: 675–678.

    Article  CAS  Google Scholar 

  34. Ljunggren, H.G. and Karre, K. 1985. Host resistance directed selectively against H-2 deficient lymphoma variants. Analysis of the mechanism. J. Exper. Med. 162: 1745–1759.

    Article  CAS  Google Scholar 

  35. Nicolic-Zugic, J. and Carbone, F.R. 1990. The effect of mutations in the MHC class I peptide binding groove on the cytotoxic T lymphocyte recognition of the Kb restricted ovalbumin determinant. Eur. J. Immunol. 20: 2431–2437.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. R. W. Johnson Pharmaceutical Research Institute, San Diego, CA, 92121

    Mariusz Milik, Dean Sauer, Anders P. Brunmark, Lunli Yuan, Antonella Vitiello, Michael R. Jackson, Per A. Peterson & Charles A. Glass

  2. Department of Molecular Biology, The Scripps Research Institute, La Jolla, CA, 92037

    Jeffrey Skolnick

Authors
  1. Mariusz Milik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dean Sauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anders P. Brunmark
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lunli Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Antonella Vitiello
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael R. Jackson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Per A. Peterson
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jeffrey Skolnick
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Charles A. Glass
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Charles A. Glass.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Milik, M., Sauer, D., Brunmark, A. et al. Application of an artificial neural network to predict specific class I MHC binding peptide sequences. Nat Biotechnol 16, 753–756 (1998). https://doi.org/10.1038/nbt0898-753

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt0898-753

This article is cited by

Search

Advanced 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