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Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach

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Abstract

Peptides bound to MHC molecules on the surface of cells convey critical information about the cellular milieu to immune system T cells. Predicting which peptides can bind an MHC molecule, and understanding their modes of binding, are important in order to design better diagnostic and therapeutic agents for infectious and autoimmune diseases. Due to the difficulty of obtaining sufficient experimental binding data for each human MHC molecule, computational modeling of MHC peptide-binding properties is necessary. This paper describes a computational combinatorial design approach to the prediction of peptides that bind an MHC molecule of known X-ray crystallographic or NMR-determined structure. The procedure uses chemical fragments as models for amino acid residues and produces a set of sequences for peptides predicted to bind in the MHC peptide-binding groove. The probabilities for specific amino acids occurring at each position of the peptide are calculated based on these sequences, and these probabilities show a good agreement with amino acid distributions derived from a MHC-binding peptide database. The method also enables prediction of the three-dimensional structure of MHC-peptide complexes. Docking, linking, and optimization procedures were performed with the XPLOR program [1].

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References

  1. Brunger, A.T., X-PLOR version 3.1, Yale University (1992).

  2. Rothbard, J.B. and Gefter, M.L., Annu. Rev. Immunol., 9 (1991) 527.

    Google Scholar 

  3. Bisset, L.R. and Fierz, W., J. Mol. Recognit., 6 (1993) 41.

    Google Scholar 

  4. Adams, H.P. and Koziol, J.A., J. Immunol. Methods, 185 (1995) 181.

    Google Scholar 

  5. Gulukota, K., Sidney, J., Sette, A. and DeLisi, C., J. Mol. Biol., 267 (1997) 1258.

    Google Scholar 

  6. Brusic, V., Rudy, G., Honeyman, G., Hammer, J. and Harrison, L., Bioinformatics, 14 (1998) 121.

    Google Scholar 

  7. Milik, M., Sauer, D., Brunmark, A.P., Yuan, L., Vitiello, A., Jackson, M.R., Peterson, P.A., Skolnick, J. and Glass, C.A., Nat. Biotechnol., 16 (1998) 753.

    Google Scholar 

  8. Hammer, J., Bono, E., Gallazzi, F., Belunis, C., Nagy, Z. and Sinigaglia, F., J. Exp. Med., 180 (1994) 2353.

    Google Scholar 

  9. Parker, K.C., Bednarek, M.A. and Coligan, J.E., J. Immunol., 152 (1994) 163.

    Google Scholar 

  10. Davenport, M.P., Ho Shon, I.A. and Hill, A.V., Immunogenetics, 42 (1995) 392.

    Google Scholar 

  11. Marshall, K.W., Wilson, K.J., Liang, J., Woods, A., Zaller, D. and Rothbard, J.B., J. Immunol., 154 (1995) 5927.

    Google Scholar 

  12. Mallios, R.R., J. Theor. Biol., 166 (1994) 167.

    Google Scholar 

  13. Mallios, R.R., Bioinformatics, 15 (1999) 432.

    Google Scholar 

  14. Mamitsuka, H., Proteins, 33 (1998) 460.

    Google Scholar 

  15. Altuvia, Y., Sette, A., Sidney, J., Southwood, S. and Margalit, H., Hum. Immunol., 58 (1997) 1.

    Google Scholar 

  16. Rognan, D., Lauemoler, S.L., Holm, A., Buus, S. and Tschnike, V., J. Med. Chem., 42 (1999) 4650.

    Google Scholar 

  17. Froloff, N., Windemuth, A. and Honig, B., Protein Sci, 6 (1997) 1293.

    Google Scholar 

  18. Sezerman, U., Vajda, S. and DeLisi, C., Protein Sci, 5 (1996) 1272.

    Google Scholar 

  19. Lim, J.S., Kim, S., Lee, H.G., Lee, K.Y., Kwon, T.J. and Kim, K., Mol. Immunol., 33 (1996) 221.

    Google Scholar 

  20. Chelvanayagam, G., Hum. Immunol., 58 (1997) 61.

    Google Scholar 

  21. Sturniolo, T., Bono, E., Ding, J., Raddrizzani, L., Tuereci, O., Sahin, U., Braxenthaler, M., Gallazzi, F., Protti, M.P., Sinigaglia, F. and Hammer, J., Nature Biotechnology, 17 (1999) 555.

    Google Scholar 

  22. Seeger, F.H., Schirle, M., Gatfield, J., Arnold, D., Keilholz, W., Nickolaus, P., Rammensee, H.G. and Stevanovic, S., Immunogenetics, 49 (1999) 571.

    Google Scholar 

  23. Madden, D.R., Garboczi, D.N. and Wiley, D.C., Cell, 75 (1993) 693.

    Google Scholar 

  24. Collins, E.J., Garboczi, D.N. and Wiley, D.C., Nature, 371 (1994) 626.

    Google Scholar 

  25. Silver, M.L., Guo, H.C., Strominger, J.L. and Wiley, D.C., Nature, 360 (1992) 367.

    Google Scholar 

  26. Madden, D.R., Annu. Rev. Immunol., 13 (1995) 587.

    Google Scholar 

  27. Smith, K.J., Reid, S.W., Harlos, K., McMichael, A.J., Stuart, D.I., Bell, J.I. and Jones, E.Y., Immunity, 4 (1996) 215.

    Google Scholar 

  28. Smith, K.J., Reid, S.W., Stuart, D.I., McMichael, A.J., Jones, E.Y. and Bell, J.I., Immunity, 4 (1996) 203.

    Google Scholar 

  29. Guo, H.C., Jardetzky, T.S., Garrett, T.P., Lane, W.S., Strominger, J.L. and Wiley, D.C., Nature, 360 (1992) 364.

    Google Scholar 

  30. Collins, E.J., Garboczi, D.N., Karpusas, M.N. and Wiley, D.C., Proc. Natl. Acad. Sci. USA, 92 (1995) 1218.

    Google Scholar 

  31. Speir, J.A., Garcia, K.C., Brunmark, A., Degano, M., Peterson, P.A., Teyton, L. and Wilson, I.A., Immunity, 8 (1998) 553.

    Google Scholar 

  32. Achour, A., Persson, K., Harris, R.A., Sundback, J., Sentman, C.L., Lindqvist, Y., Schneider, G. and Karre, K., Immunity, 9 (1998) 199.

    Google Scholar 

  33. Fremont, D.H., Matsumura, M., Stura, E.A., Peterson, P.A. and Wilson, I.A., Science, 257 (1992) 919.

    Google Scholar 

  34. Young, A.C., Zhang, W., Sacchettini, J.C. and Nathenson, S.G., Cell, 76 (1994) 39.

    Google Scholar 

  35. Zhang, C., Anderson, A. and DeLisi, C., J. Mol. Biol., 281 (1998) 929.

    Google Scholar 

  36. Parker, K.C., Biddison, W.E. and Coligan, J.E., Biochemistry, 33 (1994) 7736.

    Google Scholar 

  37. Zeng, J. and Treutlein, H.R., Protein Eng., 12 (1999) 457.

    Google Scholar 

  38. Caflisch, A., Miranker, A. and Karplus, M., J. Med. Chem., 36 (1993) 2142.

    Google Scholar 

  39. Caflisch, A. and Karplus, M., Perspect. Drug Discov. Des., 3 (1996) 51.

    Google Scholar 

  40. Caflisch, A., J. Comput. Aided Mol. Des., 10 (1996) 372.

    Google Scholar 

  41. Joseph-McCarthy, D., Hogle, J.M. and Karplus, M., Proteins, 29 (1997) 32.

    Google Scholar 

  42. Bernstein, F.C., Koetzle, T.F., Williams, G.J., Meyer, E.E., Jr., Brice, M.D., Rodgers, J.R., Kennard, O., Shimanouchi, T. and Tasumi, M., J. Mol. Biol., 112 (1977) 535.

    Google Scholar 

  43. Neria, E., Fischer, S. and Karplus, M., J. Chem. Phys., 105 (1996) 1902.

    Google Scholar 

  44. MacKerell, A.D., J. Phys. Chem., B102 (1998) 3586.

    Google Scholar 

  45. Simonson, T. and Brunger, A.T., Biochemistry, 31 (1992) 8661.

    Google Scholar 

  46. Pearlman, D.A. and Murcko, M.A., J. Med. Chem., 39 (1996) 1651.

    Google Scholar 

  47. Brusic, V., Rudy, G. and Harrison, L.C., Nucleic Acids Res., 26 (1998) 368.

    Google Scholar 

  48. Schueler-Furman, O., Elber, R. and Margalit, H., Folding & Design, 3 (1998) 549.

    Google Scholar 

  49. Thompson, J.D., Higgins, D.G. and Gibson, T.J., Comput. Appl. Biosci., 10 (1994) 19.

    Google Scholar 

  50. Henikoff, S. and Henikoff, J.G., J. Mol. Biol., 243 (1994) 574.

    Google Scholar 

  51. Schaffer, A.A., Wolf, Y.I., Ponting, C.P., Koonin, E.V., Aravind, L. and Altschul, S.F., Bioinformatics, 15 (1999) 1000.

    Google Scholar 

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Authors and Affiliations

  1. Molecular Modelling Laboratory, Ludwig Institute for Cancer Research, Royal Melbourne Hospital, P.O. Box 2008, Parkville, VIC, 3050, Australia

    Jun Zeng & Herbert R. Treutlein

  2. Genetics and Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, P.O. Royal Melbourne Hospital, Parkville, VIC, 3050, Australia

    George B. Rudy

Authors
  1. Jun Zeng
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  2. Herbert R. Treutlein
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  3. George B. Rudy
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Zeng, J., Treutlein, H.R. & Rudy, G.B. Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach. J Comput Aided Mol Des 15, 573–586 (2001). https://doi.org/10.1023/A:1011145123635

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