Springer Protocols: Abstract: Prediction of Peptide

Prediction of Peptide–MHC Binding Using Profiles

By: Pedro A. Reche³, Ellis L. Reinherz⁴

Abstract

Full Text

Download PDF (401K)

Prediction of peptide binding to major histocompatibility complex (MHC) molecules is a basis for anticipating T-cell epitopes. Peptides that bind to a given MHC molecule are related by sequence similarity. Therefore, a position-specific scoring matrix (PSSM)—also known as profile—derived from a set of aligned peptides known to bind to a given MHC molecule can be used as a predictor of both peptide–MHC binding and T-cell epitopes. In this approach, the binding potential of any peptide sequence (query) to the MHC molecule is determined by its similarity to a set of known peptide–MHC binders and can be obtained by comparing the query to the PSSM. Following structural considerations of the peptide–MHC interaction, we will describe here how to derive alignments and PSSMs that are suitable for the prediction of peptide–MHC binding.

Affiliation(s): (3) Laboratory of Immunobiology and Department of Medical Oncology, Dana-Farber Cancer Institute; and Department of Medicine, Harvard Medical School, Boston, MA
(4) Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA

Book Title: Immunoinformatics: Predicting Immunogenicity In Silico

Series: Methods in Molecular Biology | Volume: 409 | Pub. Date: Jun-21-2007 | Page Range: 185-200 | DOI: 10.1007/978-1-60327-118-9_13

Subject: Immunology

Key Words: PSSM - MHC - binding - epitopes - profile - prediction

References

Download References

1.	Margulies, D.H. 1997. Interactions of TCRs with MHC-peptide complexes: aquantitative basis for mechanistic models. Curr Opin Immunol9:390–395.

2.	Garcia, K.C., Teyton, L., and Wilson, I.A. 1999. Structural basis of T cell recognition. Annu Rev Immunol17:369–397.

3.	Wang, J.-H., and Reinherz, E.L. 2001. Structural basis of T cell recognition of peptides bound to MHC molecules. Mol Immunol38:1039–1049.

4.	Yu, K., Petrovsky, N., Schonbach, C., Koh, J.Y., and Brusic, V. 2002. Methods for prediction of peptide binding to MHC molecules: a comparative study. Mol Med8:137–148.

5.	Flower, D. 2003. Towards in silico prediction of immunogenic epitopes. Trends Immunol24:667–674.

6.	Flower, D., and Doytchinova, I.A. 2002. Immunoinformatics and the prediction of immunogenicity. Appl Bioinformatics1(4):167–176.

7.	Stern, L.J., and Wiley, D.C. 1994. Antigen peptide binding by class I and class II histocompatibility proteins. Structure2:245–251.

8.	Madden, D. 1995. The three-dimensional structure of peptide-MHC complexes. Annu Rev Immunol13:587–622.

9.	Reche, P.A., and Reinherz, E.L. 2003. Sequence variability analysis of human class I and class II MHC molecules: functional and structural correlates of amino acid polymorphisms. J Mol Biol331:623–641.

10.	D’Amaro, J., Houbiers, J.G., Drijfhout, J.W., Brandt, R.M., Schipper, R., Bavinck, J.N., Melief, C.J., and Kast, W.M. 1995. A computer program for predicting possible cytotoxic T lymphocyte epitopes based on HLA class I peptide binding motifs. Hum Immunol43:13–18.

11.	Rammensee, H.G., Bachmann, J., Emmerich, N.P.N., Bacho, O.A., and Stevanovic, S. 1999. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics50:213–219.

12.	Bouvier, M., and Wiley, D.C. 1994. Importance of peptide amino acid and carboxyl termini to the stability of MHC class I molecules. Science265:398–402.

13.	Ruppert, J., Sidney, J., Celis, E., Kubo, T., Grey, H.M., and Sette, A. 1993. Prominent role of secondary anchor residues in peptide binding to HLA-A2.1 molecules. Cell74:929–937.

14.	Gribskov, M., McLachlan, A.D., and Eisenberg, D. 1987. Profile analysis: detection of distantly related proteins. Proc Natl Acad Sci USA84:4355–4358.

15.	Gribskov, M., and Veretnik, S. 1996. Identification of sequence pattern with profile analysis. Methods Enzymol266:198–212.

16.	Pearson, W. 1997. Identifying distantly related protein sequences. Comput Appl Biosci13:325–332.

17.	Henikoff, J.G., and Henikoff, S. 1996. Using substitution probabilities to improve position-specific scoring matrices. Comput Appl Biosci12:135–143.

18.	Thompson, J.D., Higgins, D.G., and Gibson, T.J. 1994. Improved sensitivity of profile searches through the use of sequence weights and gap excision. Comput Appl Biosci10:19–29.

19.	Reche, P.A., Zhang, H., Glutting, J.-P., and Reinherz, E.L. 2005. EPIMHC: a curated database of MHC-binding peptides for customized computational vaccinology. Bioinformatics21:2140–2141.

20.	Henikoff, S., Henikoff, J.G., Alford, W.J., and Pietrokovski, S. 1995. Automated construction and graphical presentation of protein blocks from unaligned sequences. Gene163:17–26.

21.	Bailey, T.L., and Elkan, C. 1995. The value of prior knowledge in discovering motifs with MEME. Proc Int Conf Intell Syst Mol Biol3:21–29.

22.	Reche, P.A., Glutting, J.-P., and Reinherz, E.L. 2002. Prediction of MHC class I binding peptides using profile motifs. Hum Immunol63:701–709.

23.	Barber, L.D., and Parham, P. 1993. Peptide binding to major histocompatibility complex molecules. Annu Rev Cell Biol9:163–206.

24.	Thompson, J.D., Higgins, D.G., and Gibson, T.J. 1994. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weigh matrix choice. Nucleic Acids Res2:4673–4680.

25.	Henikoff, S., Henikoff, J.G., and Pietrokovski, S. 1999. Blocks+: a non-redundant database of protein alignment blocks derived from multiple compilations. Bioinformatics15:471–479.

26.	Henikoff, S., and Henikoff, J.G. 1992. Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci USA89:10915–10919.

27.	Henikoff, J.G., and Henikoff, S. 1996. Substitution probabilities to improve position-specific scoring matrices. Comput Appl Biosci12:135–143.

28.	Henikoff, S., and Henikoff, J.G. 1994. Position-based sequence weights. J Mol Biol243:574–578.

29.	Vingron, M., and Sibbald, P. 1993. Weighting in sequence space: a comparison of methods in terms of generalized sequences. Proc Natl Acad Sci USA90: 8777–8781.

30.	Sibbald, P., and Argos, P. 1990. Weighting aligned protein or nucleic acid sequences to correct for unequal representation. J Mol Biol216:813–818.

31.	Reche, P.A., Glutting, J.-P., and Reinherz, E.L. 2004. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics56:405–419.

32.	Parker, K.C., Bednarek, M.A., and Coligan, J.E. 1994. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side chains. J Immunol152:163–175.

33.	Stryhn, A., Pederson, L.O., Romme, T., Holm, A., and Buus, S. 1996. Peptide binding specificity of major histocompatibility complex class I resolved into an array of apparently independent subspecificities: quantitation by peptide libraries and improved prediction of binding. Eur J Immunol26: 1911–1918.

34.	Udaka, K., Wiesmuller, K.H., Kienle, S., Jung, G., Tamamura, H., Yamigishi,H., Okumura, K., Walden, P., Suto, T., and Kawasaki, T. 2000. An automated prediction of MHC class I-binding peptides based on positional scanning with peptide libraries. Immunogenetics51:816–828.

35.	Adams, H.P., and Koziol, J.A. 1995. Prediction of binding to MHC class I molecules. J Immunol Methods185:181–190.

36.	Gulukota, K., Sidney, J., Sette, A., and DeLisi, C. 1997. Two complementary methods for predicting peptides binding major histocompatibility complex molecules. J Mol Biol267:1258–1267.

37.	Altuvia, Y., Sette, A., Sidney, J., Southwood, S., and Margalit, H. 1997. A structure based algorithm to predict potential binding peptides to MHC molecules with hydrophobic binding pockets. Hum Immunol58:1–11.

38.	Schueler-Furman, O., Altuvia, Y., Sette, A., and Margalit, H. 2000. Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci9:1838–1846.

39.	Sturniolo, T., Bono, E., Ding, J., Raddrizzani, L., Tuereci, O., Sahin, U., Sinigaglia,F., and Hammer, J. 1999. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol17:555–561.

40.	Peters, B., Tong, W., Sidney, J., Sette, A., and Weng, Z. 2003. Examining the independent binding assumption for binding of peptide epitopes to MHC-I molecules. Bioinformatics19:1765–1772.

41.	Madden, D., Garboczi, D.N., and Wiley, D.C. 1993. The antigenic identity of peptide-MHC complexes: a comparison of the conformations of five viral peptides presented by HLA-A2. Cell75:693–708.

42.	Hennecke, J., Carfi, A., and Wiley, D.C. 2000. Structure of a covalently stabilized complex of a human alpha beta T-cell receptor, influenza HA peptide and MHC class II molecule, HLA-DR1. EMBO J19:5611–5624.

43.	Nicholls, A., Sharp, K., and Honig, B. 1991. Protein folding and association insights from the interfacial and thermodynamic properties of hydrocarbons. Proteins11:281–296.

44.	Brusic, V., Rudy, G., Kyne, A.P., and Harrison, L.C. 1998. MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res26:368–371.

45.	Bhasin, M., Singh, H., and Raghava, G.P. 2003. MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics19:665–666.

46.	Blythe, M.J., Doytchinova, I.A., and Flower, D. 2002. JenPep: a database of quantitative functional peptide data for immunology. Bioinformatics18:434–439.

47.	Schonbach, C., Koh, J.L., Flower, D., Wong, L., and Brusic, V. 2002. FIMM, a database of functional molecular immunology: update 2002. Nucleic Acids Res30:226–229.

Comments (Loading...)

Post comment/View all comments