Summary
Short peptides binding to specific human leukocyte antigen (HLA) alleles elicit immune response. These candidate peptides have potential utility in peptide vaccine design and development. The binding of peptides to allele-specific HLA molecule is estimated using competitive binding assay and biochemical binding constants. Application of this method for proteome-wide screening in parasites, viruses, and virulent bacterial strains is laborious and expensive. However, short listing of candidate peptides using prediction approaches have been realized lately. Prediction of peptide binding to HLA alleles using structural and modeling principles has gained momentum in recent years. Here, we discuss the current status of such prediction
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buus S: Description and prediction of peptide-MHC binding: the “human MHC project.” Curr Opin Immunol 11:209, 1999.
Pinilla C, Martin R, Gran B, Appel JR, Boggiano C, Wilson DB, Houghten RA: Exploring immunological specificity using synthetic peptide combinatorial libraries. Curr Opin Immunol 11:193, 1999.
Rammensee HG, Friede T, Stevanoviic S: MHC ligands and peptide motifs: first listing. Immunogenetics 41:178, 1995.
Milik M, Sauer D, Brunmark AP, Yuan L, Vitiello A, Jackson MR, Peterson PA, Skolnick J, Glass CA: Application of an artificial neural network to predict specific class I MHC binding peptide sequences. Nat Biotechnol 16:753, 1998.
Honeyman MC, Brusic V, Stone NL, Harrison LC: Neural network-based prediction of candidate T-cell epitopes. Nat Biotechnol 16:966, 1998.
Mamitsuka H: Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 33:460, 1998.
Parker KC, Shields M, DiBrino M, Brooks A, Coligan JE: Peptide binding to MHC class I molecules: implications for antigenic peptide prediction. Immunol Res 14:34, 1995.
Schafer JR, Jesdale BM, George JA, Kouttab NM, De Groot AS: Prediction of well-conserved HIV-1 ligands using a matrix-based algorithm, EpiMatrix. Vaccine 16:1880, 1998.
Jones DT, Thornton JM: Potential energy functions for threading. Curr Opin Struct Biol 6:210, 1996.
Altuvia Y, Sette A, Sidney J, Southwood S, Margalit H: A structure-based algorithm to predict potential binding peptides to MHC molecules with hydrophobic binding pockets. Hum Immunol 58:1, 1997.
Schueler-Furman O, Elber R, Margalit H: Knowledge-based structure prediction of MHC class I bound peptides: a study of 23 complexes. Fold Des 3:549, 1998.
Kangueane P, Sakharkar MK, Lim KS, Hao H, Lin K, Chee RE, Kolatkar PR: Knowledge-based grouping of modeled HLA peptide complexes. Hum Immunol 6:460, 2000.
Sette A, Sidney J, del Guercio MF, Southwood S, Ruppert J, Dahlberg C, Grey HM, Kubo RT: Peptide binding to the most frequent HLA-A class I alleles measured by quantitative molecular binding assays. Mol Immunol 31:813, 1994.
Rognan D, Lauemoller SL, Holm A, Buus S, Tschinke V: Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. J Med Chem 42:4650, 1999.
Batalia MA, Collins EJ: Peptide binding by class I and class II MHC molecules. Biopolymers 43:281, 1997.
Zhao B, Mathura VS, Rajaseger G, Moochhala S, Sakharkar MK, Kangueane P: A novel MHCp binding prediction model. Hum Immunol 64(12):1123–1143, 2003.
Rognan D, Scapozza L, Folkers G, Daser A: Molecular dynamics simulation of MHC-peptide complexes as a tool for predicting potential T cell epitopes. Biochemistry 33:11476, 1994.
Jernigan RL, Bahar I: Structure-derived potentials and protein simulations. Curr Opin Struct Biol 6:195, 1996.
Skolnick J, Jaroszewski L, Kolinski A, Godzik A: Derivation and testing of pair potentials for protein folding. When is the quasi-chemical approximation correct? Protein Sci 6:676, 1997.
Altuvia Y, Schueler O, Margalit H: Ranking potential binding peptides to MHC molecules by a computational threading approach. J Mol Biol 249:244, 1995.
Schueler-Furman O, Altuvia Y, Sette A, and Margalit H: Structure-based prediction of binding peptides to MHC class I molecules. Application to a broad range of MHC alleles. Protein Sci 9:1838, 2000.
Miyazawa S, Jernigan RL: Estimation of effective inter-residue contact energies from protein crystal structure, quasi-chemical approximation. Macromolecules 18:534, 1985.
Miyazawa S, Jernigan RL: Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J Mol Biol 256:623, 1996.
Betancourt MR, Thirumalai D: Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. Protein Sci 8:361, 1999.
Logean A, Sette A, Rognan D: Customized versus universal scoring functions: application to class I MHC-peptide binding free energy predictions. Bioorg Med Chem Lett 11:675, 2001.
Logean A, Rognan D: Recovery of known T-cell epitopes by computational scanning of a viral genome. J Comput Aided Mol Des 16:229, 2002.
Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Braxenthaler M, Gallazzi F, Protti MP, Sinigaglia F, Hammer J: Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 17:555, 1999.
Hammer J, Gallazzi F, Bono E, Karr RW, Guenot J, Valsasnini P, Nagy ZA, Sinigaglia F: Peptide binding specificity of HLA-DR4 molecules: correlation with rheumatoid arthritis association. J Exp Med 181:1847, 1995.
Gross DM, Forsthuber T, Tary-Lehmann M, Etling C, Ito K, Nagy ZA, Field JA, Steere AC, Huber BT: Identification of LFA-1 as a candidate autoantigen in treatment-resistant Lyme arthritis. Science 281:703, 1998.
Cochlovius B, Stassar M, Christ O, Raddrizzani L, Hammer J, Mytilineos I, Zoller M: In vitro and in vivo induction of a Th cell response toward peptides of the melanoma-associated glycoprotein 100 protein selected by the TEPITOPE program. J Immunol 165:4731, 2000.
Stassar MJ, Raddrizzani L, Hammer J, Zoller M: T-helper cell-response to MHC class II-binding peptides of the renal cell carcinoma-associated antigen RAGE-1. Immunobiology 203:743, 2001.
Kangueane, P Sakharkar MK: T-Epitope designer: A HLA-peptide binding prediction server. Bioinformation 1(1):21–24, 2005.
Venkatarajan MS, Braun W: New quantitative descriptors of amino acids based on multidimensional scaling of a large number of physical–chemical properties. J Mol Model 7:445, 2001.
Zeng J, Treutlein HR, Rudy GB: Predicting sequences and structures of MHC-binding peptides: a computational combinatorial approach. J Comput Aided Mol Des 15:573, 2001.
Doytchinova IA, Flower DR: Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201. J Med Chem 44:3572, 2001.
Doytchinova IA, Flower DR: A comparative molecular similarity index analysis (CoMSIA) study identifies an HLA-A2 binding supermotif. J Comput Aided Mol Des 16:535, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Humana Press Inc.
About this protocol
Cite this protocol
Kangueane, P., Sakharkar, M.K. (2007). HLA-Peptide Binding Prediction Using Structural and Modeling Principles. In: Flower, D.R. (eds) Immunoinformatics. Methods in Molecular Biology™, vol 409. Humana Press. https://doi.org/10.1007/978-1-60327-118-9_21
Download citation
DOI: https://doi.org/10.1007/978-1-60327-118-9_21
Publisher Name: Humana Press
Print ISBN: 978-1-58829-699-3
Online ISBN: 978-1-60327-118-9
eBook Packages: Springer Protocols