PAAQD: Predicting immunogenicity of MHC class I binding peptides using amino acid pairwise contact potentials and quantum topological molecular similarity descriptors

Journal of Immunological Methods

Volumn 387, Issue 1-2, 2013, Pages 293-302

  Saethang, Thammakorn ^a   Hirose, Osamu ^a   Kimkong, Ingorn ^b   Tran, Vu Anh ^a   Dang, Xuan Tho ^a   Nguyen, Lan Anh T ^a   Le, Tu Kien T ^a   Kubo, Mamoru ^a   Yamada, Yoichi ^a   Satou, Kenji ^a  

^a KANAZAWA UNIVERSITY   (Japan)

^b KASETSART UNIVERSITY   (Thailand)

Author keywords

HLA; Immunogenicity; MHC; T cell reactivity prediction; Vaccine discovery

Indexed keywords

AMINO ACID; MAJOR HISTOCOMPATIBILITY ANTIGEN CLASS 1;

AMINO ACID PAIRWISE CONTACT POTENTIAL; ANALYSIS; ANTIGEN BINDING; ANTIGEN STRUCTURE; ARTICLE; BINDING AFFINITY; COMPUTER MODEL; COMPUTER PREDICTION; IMMUNOGENICITY; PHYSICAL CHEMISTRY; PRIORITY JOURNAL; QUANTUM TOPOLOGICAL MOLECULAR SIMILARITY; T CELL REACTIVITY PREDICTOR;

AMINO ACID SEQUENCE; AMINO ACIDS; COMPUTATIONAL BIOLOGY; EPITOPES, T-LYMPHOCYTE; HISTOCOMPATIBILITY ANTIGENS CLASS I; INTERNET; OLIGOPEPTIDES; PROTEIN BINDING; REPRODUCIBILITY OF RESULTS; T-LYMPHOCYTES;

EID: 84871434208     PISSN: 00221759     EISSN: 18727905     Source Type: Journal    
DOI: 10.1016/j.jim.2012.09.016     Document Type: Article

References (44)

1. Altuvia Y., Margalit H. A structure-based approach for prediction of MHC-binding peptides. Methods 2004, 34:454.
2. Betancourt M.R., 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. 1999, 8:361.
3. Boniecki M., Rotkiewicz P., Skolnick J., Kolinski A. Protein fragment reconstruction using various modeling techniques. J. Comput. Aided Mol. Des. 2003, 17:725.
4. Brusic V., Rudy G., Harrison L.C. MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res. 1998, 26:368.
5. Cárdenas C., Villaveces J.L., Bohorquez H., Llanos E., Suarez C., Obregon M., Patarroyo M.E. Quantum chemical analysis explains hemagglutinin peptide-MHC class II molecule HLA-DRbeta1*0101 interactions. Biochem. Biophys. Res. Commun. 2004, 323:1265.
6. Cuendet M.A., Michielin O. Protein-protein interaction investigated by steered molecular dynamics: the TCR-pMHC complex. Biophys. J. 2008, 95:3575.
7. Donnes P., Kohlbacher O. SVMHC: a server for prediction of MHC-binding peptides. Nucleic Acids Res. 2006, 34:W194.
8. Falk K., Rotzschke O., Stevanovic S., Jung G., Rammensee H.G. Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 1991, 351:290.
9. Feltkamp M.C., Vierboom M.P., Kast W.M., Melief C.J. Efficient MHC class I-peptide binding is required but does not ensure MHC class I-restricted immunogenicity. Mol. Immunol. 1994, 31:1391.
10. Flower D.R., Macdonald I.K., Ramakrishnan K., Davies M.N., Doytchinova I.A. Computer aided selection of candidate vaccine antigens. Immunome Res. 2010, 6(Suppl. 2):S1.
11. Frank E., Hall M., Trigg L., Holmes G., Witten I.H. Data mining in bioinformatics using Weka. Bioinformatics 2004, 20:2479.
12. Hemmateenejad B., Yousefinejad S., Mehdipour A.R. Novel amino acids indices based on quantum topological molecular similarity and their application to QSAR study of peptides. Amino Acids 2011, 40:1169.
13. Kaas Q., Lefranc M.P. T cell receptor/peptide/MHC molecular characterization and standardized pMHC contact sites in IMGT/3Dstructure-DB. In Silico Biol. 2005, 5:505.
14. Kanduc D. Peptimmunology: immunogenic peptides and sequence redundancy. Curr. Drug Discov. Technol. 2005, 2:239.
15. Karatzoglou A., Smola A., Hornik K. kernlab-An S4 Package for Kernel Methods. JPJSS 2004, 11:1. (http://CRAN.R-project.org/package=kernlab).
16. Kawashima S., Pokarowski P., Pokarowska M., Kolinski A., Katayama T., Kanehisa M. AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. 2008, 36:D202.
17. Lundegaard C., Lamberth K., Harndahl M., Buus S., Lund O., Nielsen M. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res. 2008, 36:W509.
18. Lundegaard C., Hoof I., Lund O., Nielsen M. State of the art and challenges in sequence based T-cell epitope prediction. Immunome Res. 2010, 6(Suppl. 2):S3.
19. Madden D.R., Garboczi D.N., Wiley D.C. The antigenic identity of peptide-MHC complexes: a comparison of the conformations of five viral peptides presented by HLA-A2. Cell 1993, 75:693.
20. Miyazawa S., Jernigan R.L. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J. Mol. Biol. 1996, 256:623.
21. Miyazawa S., Jernigan R.L. Self-consistent estimation of inter-residue protein contact energies based on an equilibrium mixture approximation of residues. Proteins 1999, 34:49.
22. Nielsen M., Lundegaard C., Blicher T., Lamberth K., Harndahl M., Justesen S., Roder G., Peters B., Sette A., Lund O., Buus S. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS One 2007, 2:e796.
23. Peters B., Sidney J., Bourne P., Bui H.H., Buus S., Doh G., Fleri W., Kronenberg M., Kubo R., Lund O., Nemazee D., Ponomarenko J.V., Sathiamurthy M., Schoenberger S., Stewart S., Surko P., Way S., Wilson S., Sette A. The immune epitope database and analysis resource: from vision to blueprint. PLoS Biol. 2005, 3:e91.
24. Rammensee H., Bachmann J., Emmerich N.P., Bachor O.A., Stevanovic S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 1999, 50:213.
25. Rao X., Hoof I., Costa A.I., van Baarle D., Kesmir C. HLA class I allele promiscuity revisited. Immunogenetics 2011, 63:691.
26. Reboul C.F., Meyer G.R., Porebski B.T., Borg N.A., Buckle A.M. Epitope flexibility and dynamic footprint revealed by molecular dynamics of a pMHC-TCR complex. PLoS Comput. Biol. 2012, 8:e1002404.
27. Roomp K., Antes I., Lengauer T. Predicting MHC class I epitopes in large datasets. BMC Bioinforma. 2010, 11:90.
28. Rudolph M.G., Luz J.G., Wilson I.A. Structural and thermodynamic correlates of T cell signaling. Annu. Rev. Biophys. Biomol. Struct. 2002, 31:121.
29. Ruppert J., Sidney J., Celis E., Kubo R.T., Grey H.M., Sette A. Prominent role of secondary anchor residues in peptide binding to HLA-A2.1 molecules. Cell 1993, 74:929.
30. Schueler-Furman O., Altuvia Y., Sette A., Margalit H. Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci. 2000, 9:1838.
31. Scott D.W., De Groot A.S. Can we prevent immunogenicity of human protein drugs?. Ann. Rheum. Dis. 2010, 69(Suppl. 1):i72.
32. Simons K.T., Ruczinski I., Kooperberg C., Fox B.A., Bystroff C., Baker D. Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins 1999, 34:82.
33. Singh S.P., Mishra B.N. Ranking of binding and nonbinding peptides to MHC class I molecules using inverse folding approach: implications for vaccine design. Bioinformation 2008, 3:72.
34. Söllner J., Heinzel A., Summer G., Fechete R., Stipkovits L., Szathmary S., Mayer B. Concept and application of a computational vaccinology workflow. Immunome Res. 2010, 6(Suppl. 2):S7.
35. Stewart-Jones G.B., McMichael A.J., Bell J.I., Stuart D.I., Jones E.Y. A structural basis for immunodominant human T cell receptor recognition. Nat. Immunol. 2003, 4:657.
36. Tanaka S., Scheraga H.A. Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins. Macromolecules 1976, 9:945.
37. Tenzer S., Wee E., Burgevin A., Stewart-Jones G., Friis L., Lamberth K., Chang C.H., Harndahl M., Weimershaus M., Gerstoft J., Akkad N., Klenerman P., Fugger L., Jones E.Y., McMichael A.J., Buus S., Schild H., van Endert P., Iversen A.K. Antigen processing influences HIV-specific cytotoxic T lymphocyte immunodominance. Nat. Immunol. 2009, 10:636.
38. Toussaint N.C., Donnes P., Kohlbacher O. A mathematical framework for the selection of an optimal set of peptides for epitope-based vaccines. PLoS Comput. Biol. 2008, 4:e1000246.
39. Tung C.W., Ho S.Y. POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties. Bioinformatics 2007, 23:942.
40. Tung C.W., Ziehm M., Kamper A., Kohlbacher O., Ho S.Y. POPISK: T-cell reactivity prediction using support vector machines and string kernels. BMC Bioinforma. 2011, 12:446.
41. Vacic V., Iakoucheva L.M., Radivojac P. Two Sample Logo: a graphical representation of the differences between two sets of sequence alignments. Bioinformatics 2006, 22:1536.
42. Van Regenmortel M.H. Antigenicity and immunogenicity of synthetic peptides. Biologicals 2001, 29:209.
43. Wan J., Liu W., Xu Q., Ren Y., Flower D.R., Li T. SVRMHC prediction server for MHC-binding peptides. BMC Bioinforma. 2006, 7:463.
44. Wu X., Xu X., Gu R., Wang Z., Chen H., Xu K., Zhang M., Hutton J., Yang T. Prediction of HLA class I-restricted T-cell epitopes of islet autoantigen combined with binding and dissociation assays. Autoimmunity 2012, 45:176.