Proteomics in Vaccinology and Immunobiology: An Informatics Perspective of the Immunone

Journal of Biomedicine and Biotechnology

Volumn 2003, Issue 5, 2003, Pages 267-290

  Doytchinova, Irini A ^a   Taylor, Paul ^a   Flower, Darren R ^a  

^a EDWARD JENNER INST FOR VACC RES   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

VACCINE;

BACTERIAL GENOME; GENOMICS; HOST PATHOGEN INTERACTION; HUMAN; IMMUNOBIOLOGY; IMMUNOCHEMISTRY; NONHUMAN; PROTEIN LOCALIZATION; PROTEIN PROTEIN INTERACTION; PROTEOMICS; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; VIRUS GENOME;

ALIA; BACTERIA (MICROORGANISMS);

EID: 0942265524     PISSN: 11107243     EISSN: None     Source Type: Journal    
DOI: 10.1155/S1110724303209232     Document Type: Review

References (169)

1. Paine K, Flower DR. The lipocalin website. Biochim Biophys Acta. 2000;1482(1-2):351-352.
2. Ji H, Zhou Q, Wen F, Xia H, Lu X, Li Y. AsMamDB: an alternative splice database of mammals. Nucleic Acids Res. 2001;29(1):260-263.
3. Perler FB. InBase: the Intein Database. Nucleic Acids Res. 2002;30(1):383-384.
4. Wasinger VC, Cordwell SJ, Cerpa-Poljak A, et al. Progress with gene-product mapping of the mollicutes: Mycoplasma genitalium. Electrophoresis. 1995;16(7):1090-1094.
5. Anderson NG, Anderson L. The human protein index. Clin Chem. 1982;28(4 pt 2):739-748.
6. Holtappels R, Grzimek NK, Thomas D, Reddehase MJ. Early gene m18, a novel player in the immune response to murine cytomegalovirus. J Gen Virol. 2002;83(pt 2):311-316.
7. Holtappels R, Thomas D, Podlech J, Reddehase MJ. Two antigenic peptides from genes m123 and m164 of murine cytomegalovirus quantitatively dominate CD8 T-cell memory in the H-2d haplotype. J Virol. 2002;76(1):151-164.
8. Dalton JP, Mulcahy G. Parasite vaccines-a reality? Vet Parasitol. 2001;98(1-3):149-167.
9. Chakravarti DN, Fiske MJ, Fletcher LD, Zagursky RJ. Mining genomes and mapping proteomes: identification and characterization of protein subunit vaccines. Dev Biol (Basel). 2000;103:81-90.
10. Toby GG, Golemis EA. Using the yeast interaction trap and other two-hybrid-based approaches to study protein-protein interactions. Methods. 2001; 24(3):201-217.
11. Templin MF, Stoll D, Schrenk M, Traub PC, Vohringer CF, Joos TO. Protein microarray technology. Trends Biotechnol. 2002;20(4):160-166.
12. Walker J, Flower D, Rigley K. Microarrays in hematology. Curr Opin Hematol. 2002;9(1):23-29.
13. Grandi G. Antibacterial vaccine design using genomics and proteomics. Trends Biotechnol. 2001; 19(5):181-188.
14. Rathod PK, Ganesan K, Hayward RE, Bozdech Z, DeRisi JL. DNA microarrays for malaria. Trends Parasitol. 2002;18(1):39-45.
15. Knox DP, Redmond DL, Skuce PJ, Newlands GF. The contribution of molecular biology to the development of vaccines against nematode and trematode parasites of domestic ruminants. Vet Parasitol. 2001;101(3-4):311-335.
16. Glynne RJ, Watson SR. The immune system and gene expression microarrays-new answers to old questions. J Pathol. 2001;195(1):20-30.
17. Dhiman N, Bonilla R, O'Kane DJ, Poland GA. Gene expression microarrays: a 21st century tool for directed vaccine design. Vaccine. 2001;20(1-2):22-30.
18. de Veer MJ, Holko M, Frevel M, et al. Functional classification of interferon-stimulated genes identified using microarrays. J Leukoc Biol. 2001; 69(6):912-920.
19. Emslie KR, Molloy MP, Barardi CR, et al. Serotype classification and characterisation of the rotavirus SA11 VP6 protein using mass spectrometry and two-dimensional gel electrophoresis. Funct Integr Genomics. 2000;1(1):12-24.
20. Thomas JJ, Bakhtiar R, Siuzdak G. Mass spectrometry in viral proteomics. Acc Chem Res. 2000; 33(3):179-187.
21. Yao X, Freas A, Ramirez J, Demirev PA, Fenselau C. Proteolytic 180 labeling for comparative proteomics: model studies with two serotypes of adenovirus. Anal Chem. 2001;73(13):2836-2842.
22. Toda T, Sugimoto M, Omori A, Matsuzaki T, Furuichi Y, Kimura N. Proteomic analysis of Epstein-Barr virus-transformed human B-lymphoblastoid cell lines before and after immortalization. Electrophoresis. 2000;21(9):1814-1822.
23. Diaz JJ, Giraud S, Greco A. Alteration of ribosomal protein maps in herpes simplex virus type 1 infection. J Chromatogr B Analyt Technol Biomed Life Sci. 2002;771(1-2):237-249.
24. Greco A, Bienvenut W, Sanchez JC, et al. Identification of ribosome-associated viral and cellular basic proteins during the course of infection with herpes simplex virus type 1. Proteomics. 2001;1(4):545-549.
25. Rodriguez JM, Salas ML, Santaren JF. African swine fever virus-induced polypeptides in porcine alveolar macrophages and in Vero cells: two-dimensional gel analysis. Proteomics. 2001;1(11):1447-1456.
26. Blattner FR, Plunkett G 3rd, Bloch CA, et al. The complete genome sequence of Escherichia coli K-12. Science. 1997;277(5331):1453-1474.
27. Fleischmann RD, Adams MD, White O, et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science. 1995; 269(5223):496-512.
28. Fraser CM, Gocayne JD, White O, et al. The minimal gene complement of Mycoplasma genitalium. Science. 1995;270(5235):397-403.
29. Bult CJ, White O, Olsen GJ, et al. Complete genome sequence of the methanogenic archaeon, Methanococcus jannaschii. Science. 1996;273(5278): 1058-1073.
30. Tomb JF, White O, Kerlavage AR, et al. The complete genome sequence of the gastric pathogen Helicobacter pylori. Nature. 1997;388(6642):539-547.
31. Paine K, Flower DR. Bacterial bioinformatics: pathogenesis and the genome. J Mol Microbiol Biotechnol. 2002;4(4):357-365.
32. O'Connor CD, Farris M, Fowler R, Qi SY. The proteome of Salmonella enterica serovar Typhimurium: current progress on its determination and some applications. Electrophoresis. 1997;18(8): 1483-1490.
33. Hecker M, Engelmann S. Proteomics, DNA arrays and the analysis of still unknown regulons and unknown proteins of Bacillus subtilis and pathogenic gram-positive bacteria. Int J Med Microbiol. 2000;290(2):123-134.
34. Regula JT, Ueberle B, Boguth G, et al. Towards a two-dimensional proteome map of Mycoplasma pneumoniae. Electrophoresis. 2000;21(17):3765-3780.
35. Vandahl BB, Birkelund S, Demol H, et al. Proteome analysis of the Chlamydia pneumoniae elementary body. Electrophoresis. 2001;22(6):1204-1223.
36. Shaw AC, Gevaert K, Demol H, et al. Comparative proteome analysis of Chlamydia trachomatis serovar A, D, and L2. Proteomics. 2002;2(2):164-186.
37. Piechaczek K, Dobrindt U, Schierhorn A, Fischer GS, Hecker M, Hacker J. Influence of pathogenicity islands and the minor leuX-encoded tRNA5Leu on the proteome pattern of the uropathogenic Escherichia coli strain 536. Int J Med Microbiol. 2000;290(1):75-84.
38. Malhotra S, Silo-Suh LA, Mathee K, Ohman DE. Proteome analysis of the effect of mucoid conversion on global protein expression in Pseudomonas aeruginosa strain PAO1 shows induction of the disulfide bond isomerase, dsbA. J Bacteriol. 2000;182(24):6999-7006.
39. Rosenkrands I, King A, Weldingh K, Moniatte M, Moertz E, Andersen P. Towards the proteome of Mycobacterium tuberculosis. Electrophoresis. 2000;21(17):3740-3756.
40. Rosenkrands I, Weldingh K, Jacobsen S, et al. Mapping and identification of Mycobacterium tuberculosis proteins by two-dimensional gel electrophoresis, microsequencing and immunodetection. Electrophoresis. 2000;21(5):935-948.
41. Jungblut PR, Muller EC, Mattow J, Kaufmann SH. Proteomics reveals open reading frames in Mycobacterium tuberculosis H37Rv not predicted by genomics. Infect Immun. 2001;69(9):5905-5907.
42. Jungblut PR, Schaible UE, Mollenkopf HJ, et al. Comparative proteome analysis of Mycobacterium tuberculosis and Mycobacterium bovis BCG strains: towards functional genomics of microbial pathogens. Mol Microbiol. 1999;33(6):1103-1117.
43. Mattow J, Jungblut PR, Schaible UE, et al. Identification of proteins from Mycobacterium tuberculosis missing in attenuated Mycobacterium bovis BCG strains. Electrophoresis. 2001;22(14):2936-2946.
44. Wong DK, Lee BY, Horwitz MA, Gibson BW. Identification of fur, aconitase, and other proteins expressed by Mycobacterium tuberculosis under conditions of low and high concentrations of iron by combined two-dimensional gel electrophoresis and mass spectrometry. Infect Immun. 1999;67(1):327-336.
45. Monahan IM, Betts J, Banerjee DK, Butcher PD. Differential expression of mycobacterial proteins following phagocytosis by macrophages. Microbiology. 2001;147(pt 2):459-471.
46. Betts JC, Lukey PT, Robb LC, McAdam RA, Duncan K. Evaluation of a nutrient starvation model of Mycobacterium tuberculosis persistence by gene and protein expression profiling. Mol Microbiol. 2002;43(3):717-731.
47. Betts JC, Dodson P, Quan S, et al. Comparison of the proteome of Mycobacterium tuberculosis strain H37Rv with clinical isolate CDC 1551. Microbiology. 2000;146(pt 12):3205-3216.
48. Covert BA, Spencer JS, Orme IM, Belisle JT. The application of proteomics in defining the T cell antigens of Mycobacterium tuberculosis. Proteomics. 2001;1(4):574-586.
49. Langenberg M-L, Tytgat GNJ, Schipper MEI, Rietra PJGM, Zanen HC. Campylobacter-like organism in the stomach of patients and healthy individuals. Lancet. 1984;ii(1348).
50. Utt M, Nilsson I, Ljungh A, Wadstrom T. Identification of novel immunogenic proteins of Helicobacter pylori by proteome technology. J Immunol Methods. 2002;259(1-2):1-10.
51. McAtee CP, Lim MY, Fung K, et al. Identification of potential diagnostic and vaccine candidates of Helicobacter pylori by two-dimensional gel electrophoresis, sequence analysis, and serum profiling. Clin Diagn Lab Immunol. 1998;5(4):537-542.
52. Kimmel B, Bosserhoff A, Frank R, Gross R, Goebel W, Beier D. Identification of immunodominant antigens from Helicobacter pylori and evaluation of their reactivities with sera from patients with different gastroduodenal pathologies. Infect Immun. 2000;68(2):915-920.
53. Jungblut PR, Bumann D, Haas G, et al. Comparative proteome analysis of Helicobacter pylori. Mol Microbiol. 2000;36(3):710-725.
54. Nilsson I, Utt M, Nilsson HO, Ljungh A, Wadstrom T. Two-dimensional electrophoretic and immunoblot analysis of cell surface proteins of spiral-shaped and coccoid forms of Helicobacter pylori. Electrophoresis. 2000;21(13):2670-2677.
55. Chakravarti DN, Fiske MJ, Fletcher LD, Zagursky RJ. Application of genomics and proteomics for identification of bacterial gene products as potential vaccine candidates. Vaccine. 2000;19(6):601-612.
56. Haas G, Karaali G, Ebermayer K, et al. Immunoproteomics of Helicobacter pylori infection and relation to gastric disease. Proteomics. 2002;2(3):313-324.
57. McAtee CP, Hoffman PS, Berg DE. Identification of differentially regulated proteins in metronidozole resistant Helicobacter pylori by proteome techniques. Proteomics. 2001;1(4):516-521.
58. Yoder OC, Turgeon BG. Fungal genomics and pathogenicity. Curr Opin Plant Biol. 2001;4(4):315-321.
59. Yoder OC, Turgeon BG. Molecular genetic evaluation of fungal molecules for roles in pathogenesis to plants. J Genet. 1996;75:425-440.
60. Oliver R, Osbourn A. Molecular dissection of fungal phytopathogenicity. Microbiology. 1995;141 (pt 1):1-9.
61. Hogan LH, Klein BS, Levitz SM. Virulence factors of medically important fungi. Clin Microbiol Rev. 1996;9(4):469-488.
62. Lim D, Hains P, Walsh B, Bergquist P, Nevalainen H. Proteins associated with the cell envelope of Trichoderma reesei: a proteomic approach. Proteomics. 2001;1(7):899-909.
63. Gutierrez JA. Genomics: from novel genes to new therapeutics in parasitology. Int J Parasitol. 2000;30(3):247-252.
64. Colley DG. Parasitic diseases: opportunities and challenges in the 21st century. Mem Inst Oswaldo Cruz. 2000;95(suppl 1):79-87.
65. Knox DP, Redmond DL, Skuce PJ, Newlands GF. The contribution of molecular biology to the development of vaccines against nematode and trematode parasites of domestic ruminants. Vet Parasitol. 2001;101(3-4):311-335.
66. Jefferies JR, Campbell AM, van Rossum AJ, Barrett J, Brophy PM. Proteomic analysis of Fasciola hepatica excretory-secretory products. Proteomics. 2001;1(9):1128-1132.
67. Almeida R, Norrish A, Levick M, et al. From genomes to vaccines: Leishmania as a model. Philos Trans R Soc Lond B Biol Sci. 2002;357(1417):5-11.
68. Thiel M, Bruchhaus I. Comparative proteome analysis of Leishmania donovani at different stages of transformation from promastigotes to amastigotes. Med Microbiol Immunol (Berl). 2001;190(1-2):33-36.
69. Cohen AM, Rumpel K, Coombs GH, Wastling JM. Characterisation of global protein expression by two-dimensional electrophoresis and mass spectrometry: proteomics of Toxoplasma gondii. Int J Parasitol. 2002;32(1):39-51.
70. Dlugonska H, Dytnerska K, Reichmann G, Stachelhaus S, Fischer HG. Towards the Toxoplasma gondii proteome: position of 13 parasite excretory antigens on a standardized map of two-dimensionally separated tachyzoite proteins. Parasitol Res. 2001;87(8):634-637.
71. Fletcher JM, Nair SP, Ward JM, Henderson B, Wilson M. Analysis of the effect of changing environmental conditions on the expression patterns of exported surface-associated proteins of the oral pathogen Actinobacillus actinomycetemcomitans. Microb Pathog. 2001;30(6):359-368.
72. Monahan IM, Betts J, Banerjee DK, Butcher PD. Differential expression of mycobacterial proteins following phagocytosis by macrophages. Microbiology. 2001;147(pt 2):459-471.
73. Ragno S, Romano M, Howell S, Pappin DJ, Jenner PJ, Colston MJ. Changes in gene expression in macrophages infected with Mycobacterium tuberculosis: a combined transcriptomic and proteomic approach. Immunology. 2001;104(1):99-108.
74. Kovarova H, Halada P, Man P, et al. Proteome study of Francisella tularensis live vaccine strain-containing phagosome in Bcg/Nramp1 congenic macrophages: resistant allele contributes to permissive environment and susceptibility to infection. Proteomics. 2002;2(1):85-93.
75. Pizarro-Cerda J, Jonquieres R, Gouin E, Vandekerckhove J, Garin J, Cossart P. Distinct protein patterns associated with Listeria monocytogenes InlA- or InlB-phagosomes. Cell Microbiol. 2002;4(2):101-115.
76. Truffa-Bachi P, Lefkovits I, Frey JR. Proteomic analysis of T cell activation in the presence of cyclosporin A: immunosuppressor and activator removal induces de novo protein synthesis [Erratum in Mol Immunol. 2000;37(5):261]. Mol Immunol. 2000;37(1-2):21-28.
77. Truffa-Bachi P, Lefkovits I, Frey JR. Proteomic analysis of T cell activation in the presence of cyclosporin A: immunosuppressor and activator removal induces de novo protein synthesis [Erratum in Mol Immunol. 2000;37(5):261]. Mol Immunol. 2000;37(1-2):21-28.
78. Nyman TA, Rosengren A, Syyrakki S, Pellinen TP, Rautajoki K, Lahesmaa R. A proteome database of human primary T helper cells. Electrophoresis. 2001;22(20):4375-4382.
79. Fratelli M, Demol H, Puype M, et al. Identification by redox proteomics of glutathionylated proteins in oxidatively stressed human T lymphocytes. Proc Natl Acad Sci USA. 2002;99(6):3505-3510.
80. Williams A. Applications of computer software for the interpretation and management of mass spectrometry data in pharmaceutical science. Curr Top Med Chem. 2002;2(1):99-107.
81. Sidhu KS, Sangvanich P, Brancia FL, et al. Bioinformatic assessment of mass spectrometric chemical derivatisation techniques for proteome database searching. Proteomics. 2001;1(11):1368-1377.
82. Vihinen M. Bioinformatics in proteomics. Biomol Eng. 2001;18(5):241-248.
83. Chakravarti DN, Chakravarti B, Moutsatsos I. Informatic tools for proteome profiling. Biotechniques. 2002;(suppl):4-15.
84. Nussbaum AK, Dick TP, Keilholz W, et al. Cleavage motifs of the yeast 20S proteasome beta subunits deduced from digests of enolase 1. Proc Natl Acad Sci USA. 1998;95(21):12504-12509.
85. Holzhutter HG, Frommel C, Kloetzel PM. A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20S proteasome. J Mol Biol. 1999;286(4):1251-1265.
86. Kuttler C, Nussbaum AK, Dick TP, Rammensee HG, Schild H, Hadeler KP. An algorithm for the prediction of proteasomal cleavages. J Mol Biol. 2000;298(3):417-429.
87. Nussbaum AK, Kuttler C, Hadeler KP, Rammensee HG, Schild H. PAProC: a prediction algorithm for proteasomal cleavages available on the WWW. Immunogenetics. 2001;53(2):87-94.
88. Kesmir C, Nussbaum AK, Schild H, Detours V, Brunak S. Prediction of proteasome cleavage motifs by neural networks. Protein Eng. 2002;15(4):287-296.
89. Daniel S, Brusic V, Caillat-Zucman S, et al. Relationship between peptide selectivities of human transporters associated with antigen processing and HLA class I molecules. J Immunol. 1998;161(2):617-624.
90. Brusic V, van Endert P, Zeleznikow J, Daniel S, Hammer J, Petrovsky N. A neural network model approach to the study of human TAP transporter. In Silico Biol. 1999;1(2):109-121.
91. Chapman HA. Endosomal proteolysis and MHC class II function. Curr Opin Immunol. 1998;10(1): 93-102.
92. Sette A, Buus S, Appella E, et al. Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis. Proc Natl Acad Sci USA. 1989;86(9):3296-3300.
93. D'Amaro J, Houbiers JG, Drijfhout JW, et al. A computer program for predicting possible cytotoxic T lymphocyte epitopes based on HLA class I peptide-binding motifs. Hum Immunol. 1995; 43(1):13-18.
94. Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics. 1999;50(3-4):213-219.
95. Parker KC, Shields M, DiBrino M, Brooks A, Coligan JE. Peptide binding to MHC class I molecules: implications for antigenic peptide prediction. Immunol Res. 1995;14(1):34-57.
96. Parker KC, DiBrino M, Hull L, Coligan JE. The beta 2-microglobulin dissociation rate is an accurate measure of the stability of MHC class I heterotrimers and depends on which peptide is bound. J Immunol. 1992;149(6):1896-1904.
97. Parker KC, Bednarek MA, Coligan JE. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol. 1994;152(1):163-175.
98. Parker KC, Bednarek MA, Hull LK, et al. Sequence motifs important for peptide binding to the human MHC class I molecule, HLA-A2. J Immunol. 1992;149(11):3580-3587.
99. Parker KC, Carreno BM, Sestak L, Utz U, Biddison WE, Coligan JE. Peptide binding to HLA-A2 and HLA-B27 isolated from Escherichia coli. Reconstitution of HLA-A2 and HLA-B27 heavy chain/beta 2-microglobulin complexes requires specific peptides. J Biol Chem. 1992;267(8):5451-5459.
100. DiBrino M, Parker KC, Shiloach J, et al. Endogenous peptides bound to HLA-A3 possess a specific combination of anchor residues that permit identification of potential antigenic peptides. Proc Natl Acad Sci USA. 1993;90(4):1508-1512.
101. DiBrino M, Parker KC, Margulies DH, et al. The HLA-B14 peptide binding site can accommodate peptides with different combinations of anchor residues. J Biol Chem. 1994;269(51):32426-32434.
102. Parker KC, Biddison WE, Coligan JE. Pocket mutations of HLA-B27 show that anchor residues act cumulatively to stabilize peptide binding. Biochemistry. 1994;33(24):7736-7743.
103. DiBrino M, Parker KC, Margulies DH, et al. Identification of the peptide binding motif for HLA-B44, one of the most common HLA-B alleles in the Caucasian population. Biochemistry. 1995;34(32): 10130-10138.
104. DiBrino M, Tsuchida T, Turner RV, Parker KC, Coligan JE, Biddison WE. HLA-A1 and HLA-A3 T cell epitopes derived from influenza virus proteins predicted from peptide binding motifs. J Immunol. 1993;151(11):5930-5935.
105. + cytotoxic T lymphocytes restricted by HLA-A3: evidence for cross-reactivity with an environmental microorganism. J Neuroimmunol. 1997;73(1-2):7-14.
106. Bisset LR, Fierz W. Using a neural network to identify potential HLA-DR1 binding sites within proteins. J Mol Recognit. 1993;6(1):41-48.
107. Brusic V, Schonbach C, Takiguchi M, Ciesielski V, Harrison LC. Application of genetic search in derivation of matrix models of peptide binding to MHC molecules. Proc Int Conf Intell Syst Mol Biol. 1997;5:75-83.
108. Harrison LC, Honeyman MC, Trembleau S, et al. A peptide-binding motif for I-A(g7), the class II major histocompatibility complex (MHC) molecule of NOD and Biozzi AB/H mice. J Exp Med. 1997;185(6):1013-1021.
109. Honeyman MC, Brusic V, Harrison LC. Strategies for identifying and predicting islet autoantigen T-cell epitopes in insulin-dependent diabetes mellitus. Ann Med. 1997;29(5):401-404.
110. Honeyman MC, Brusic V, Stone NL, Harrison LC. Neural network-based prediction of candidate T-cell epitopes. Nat Biotechnol. 1998;16(10):966-969.
111. Brusic V, Rudy G, Harrison LC. MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res. 1998;26(1):368-371.
112. Rosenfeld R, Zheng Q, Vajda S, DeLisi C. Flexible docking of peptides to class I major-histocompatibility-complex receptors. Genet Anal. 1995;12(1):1-21.
113. Sezerman U, Vajda S, DeLisi C. Free energy mapping of class I MHC molecules and structural determination of bound peptides. Protein Sci. 1996;5(7):1272-1281.
114. Vasmatzis G, Zhang C, Cornette JL, DeLisi C. Computational determination of side chain specificity for pockets in class I MHC molecules. Mol Immunol. 1996;33(16):1231-1239.
115. Rognan D, Reddehase MJ, Koszinowski UH, Folkers G. Molecular modeling of an antigenic complex between a viral peptide and a class I major histocompatibility glycoprotein. Proteins. 1992; 13(1):70-85.
116. Rognan D, Zimmermann N, Jung G, Folkers G. Molecular dynamics study of a complex between the human histocompatibility antigen HLA-A2 and the IMP58-66 nonapeptide from influenza virus matrix protein. Eur J Biochem. 1992;208(1):101-113.
117. 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. 1994;33(38):11476-11485.
118. Caflisch A, Niederer P, Anliker M. Monte Carlo docking of oligopeptides to proteins. Proteins. 1992;13(3):223-230.
119. Lim JS, Kim S, Lee HG, Lee KY, Kwon TJ, Kim K. Selection of peptides that bind to the HLA-A2.1 molecule by molecular modelling. Mol Immunol. 1996;33(2):221-230.
120. Androulakis IP, Nayak NN, Ierapetritou MG, Monos DS, Floudas CA. A predictive method for the evaluation of peptide binding in pocket 1 of HLA-DRB1 via global minimization of energy interactions. Proteins. 1997;29(1):87-102.
121. Froloff N, Windemuth A, Honig B. On the calculation of binding free energies using continuum methods: application to MHC class I protein-peptide interactions. Protein Sci. 1997;6(6):1293-1301.
122. Arora N, Bashford D. Solvation energy density occlusion approximation for evaluation of desolvation penalties in biomolecular interactions. Proteins. 2001;43(1):12-27.
123. Doytchinova IA, Blythe MJ, Flower DR. Additive method for the prediction of protein-peptide binding affinity. Application to the MHC class I molecule HLA-A*0201. J Proteome Res. 2002;1(3):263-272.
124. Doytchinova IA, Flower DR. Toward the quantitative prediction of T-cell epitopes: CoMFA and CoMSIA studies of peptides with affinity to the class I MHC molecule HLA-A*0201. J Med Chem. 2001;44(22):3572-3581.
125. Doytchinova IA, Flower DR. Physicochemical explanation of peptide binding to HLA-A*0201 major histocompatibility complex: a three-dimensional quantitative structure-activity relationship study. Proteins. 2002;48(3):505-518.
126. 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. 1999;42(22):4650-4658.
127. 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. 2001;11(5):675-679.
128. Altuvia Y, Schueler O, Margalit H. Ranking potential binding peptides to MHC molecules by a computational threading approach. J Mol Biol. 1995;249(2):244-250.
129. 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. 1997;58(1):1-11.
130. 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. 1996;256(3):623-644.
131. 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(9):1838-1846.
132. 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. 1999;8(2):361-369.
133. Stryhn A, Pedersen LO, Romme T, Holm CB, Holm A, Buus S. 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 Immunol. 1996;26(8):1911-1918.
134. Stevens J, Wiesmuller KH, Walden P, Joly E. Peptide length preferences for rat and mouse MHC class I molecules using random peptide libraries. Eur J Immunol. 1998;28(4):1272-1279.
135. Stevens J, Wiesmuller KH, Barker PJ, Walden P, Butcher GW, Joly E. Efficient generation of major histocompatibility complex class I-peptide complexes using synthetic peptide libraries. J Biol Chem. 1998;273(5):2874-2884.
136. Zhao Y, Gran B, Pinilla C, et al. Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic TCR and MHC peptide ligands. J Immunol. 2001;167(4):2130-2141.
137. Pinilla C, Rubio-Godoy V, Dutoit V, et al. Combinatorial peptide libraries as an alternative approach to the identification of ligands for tumor-reactive cytolytic T lymphocytes. Cancer Res. 2001; 61(13):5153-5160.
138. Udaka K, Wiesmuller KH, Kienle S, et al. An automated prediction of MHC class I-binding peptides based on positional scanning with peptide libraries. Immunogenetics. 2000;51(10):816-828.
139. Blythe MJ, Doytchinova IA, Flower DR. JenPep: a database of quantitative functional peptide data for immunology. Bioinformatics. 2002;18(3):434-439.
140. Van Regenmortel MH, Daney de Marcillac G. An assessment of prediction methods for locating continuous epitopes in proteins. Immunol Lett. 1988;17(2):95-107.
141. Ferreira-da-Cruz Mde F, Giovanni-de-Simone S, Banic DM, Canto-Cavalheiro M, Camus D, Daniel-Ribeiro CT. Can software be used to predict antigenic regions in Plasmodium falciparum peptides? Parasite Immunol. 1996;18(3):159-161.
142. Thornton JM, Edwards MS, Taylor WR, Barlow DJ. Location of "continuous" antigenic determinants in the protruding regions of proteins. EMBO J. 1986;5(2):409-413.
143. Barlow DJ, Edwards MS, Thornton JM. Continuous and discontinuous protein antigenic determinants. Nature. 1986;322(6081):747-748.
144. Van Regenmortel MH, Pellequer JL. Predicting antigenic determinants in proteins: looking for unidimensional solutions to a three-dimensional problem? Pept Res. 1994;7(4):224-228.
145. Alix AJ. Predictive estimation of protein linear epitopes by using the program PEOPLE. Vaccine. 1999;18(3-4):311-314.
146. Pellequer JL, Westhof E. PREDITOP: a program for antigenicity prediction. J Mol Graph. 1993;11(3): 204-210.
147. Saleh MT, Fillon M, Brennan PJ, Belisle JT. Identification of putative exported/secreted proteins in prokaryotic proteomes. Gene. 2001; 269(1-2):195-204.
148. Kumar A, Agarwal S, Heyman JA, et al. Subcellular localization of the yeast proteome. Genes Dev. 2002;16(6):707-719.
149. Gromiha MM. A simple method for predicting transmembrane alpha helices with better accuracy. Protein Eng. 1999;12(7):557-561.
150. Nishikawa K, Ooi T. Correlation of the amino acid composition of a protein to its structural and biological characters. J Biochem (Tokyo). 1982; 91(5):1821-1824.
151. Eisenhaber F, Frommel C, Argos P. Prediction of secondary structural content of proteins from their amino acid composition alone. II. The paradox with secondary structural class. Proteins. 1996; 25(2):169-179.
152. Chiapello H, Ollivier E, Landes-Devauchelle C, Nitschke P, Risler JL. Codon usage as a tool to predict the cellular location of eukaryotic ribosomal proteins and aminoacyl-tRNA synthetases. Nucleic Acids Res. 1999; 27(14):2848-2851.
153. Chou KC, Elrod DW. Using discriminant function for prediction of subcellular location of prokaryotic proteins. Biochem Biophys Res Commun. 1998; 252(1):63-68.
154. Cedano J, Aloy P, Pẽrez-Pons JA, Querol E. Relation between amino acid composition and cellular location of proteins. J Mol Biol. 1997;266(3):594-600.
155. Nakashima H, Nishikawa K. Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol. 1994;238(1):54-61.
156. Andrade MA, O'Donoghue SI, Rost B. Adaptation of protein surfaces to subcellular location. J Mol Biol. 1998;276(2):517-525.
157. Reinhardt A, Hubbard T. Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res. 1998;26(9):2230-2236.
158. Nakai K. Predicting various targeting signals in amino acid sequences. Bull Inst Chem Res Kyoto Univ. 1991;69:269-291.
159. Nakai K. Protein sorting signals and prediction of subcellular localization. Adv Protein Chem. 2000; 54:277-344.
160. Briggs MS, Gierasch LM, Zlotnick A, Lear JD, DeGrado WF. In vivo function and membrane binding properties are correlated for Escherichia coli lamB signal peptides. Science. 1985; 228(4703):1096-1099.
161. von Heijne G. Signal sequences. The limits of variation. J Mol Biol. 1985;184(1):99-105.
162. Martoglio B, Dobberstein B. Signal sequences: more than just greasy peptides. Trends Cell Biol. 1998;8(10):410-415.
163. Sjostrom M, Wold S, Wieslander A, Rilfors L. Signal peptide amino acid sequences in Escherichia coli contain information related to final protein localization. A multivariate data analysis. EMBO J. 1987;6(3):823-831.
164. Edman M, Jarhede T, Sjostrom M, Wieslander A. Different sequence patterns in signal peptides from mycoplasmas, other gram-positive bacteria, and Escherichia coli: a multivariate data analysis. Proteins. 1999;35(2):195-205.
165. Nielsen H, Engelbrecht J, Brunak S, von Heijne G. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng. 1997;10(1):1-6.
166. Jagla B, Schuchhardt J. Adaptive encoding neural networks for the recognition of human signal peptide cleavage sites. Bioinformatics. 2000;16(3):245-250.
167. Mouritsen S, Dalum I, Engel AM, et al. MHC class II-bound self-peptides can be effectively separated by isoelectric focusing and bind optimally to their MHC class II restriction elements around pH 5.0. Immunology. 1994;82(4):529-534.
168. Dongre AR, Kovats S, deRoos P, et al. In vivo MHC class II presentation of cytosolic proteins revealed by rapid automated tandem mass spectrometry and functional analyses. Eur J Immunol. 2001;31(5):1485-1494.
169. Purcell AW, Gorman JJ. The use of post-source decay in matrix-assisted laser desorption/ionisation mass spectrometry to delineate T cell determinants. J Immunol Methods. 2001;249(1-2):17-31.