205
Views
13
CrossRef citations to date
0
Altmetric
Reviews

Towards in silico design of epitope-based vaccines

&
Pages 1047-1060 | Published online: 28 Aug 2009

Bibliography

  • Moutschen M, Léonard P, Sokal EM, et al. Phase I/II studies to evaluate safety and immunogenicity of a recombinant gp350 Epstein-Barr virus vaccine in healthy adults. Vaccine 2007;25:4697-705
  • Goepfert PA, Tomaras GD, Horton H, et al. Durable HIV-1 antibody and T-cell responses elicited by an adjuvanted multi-protein recombinant vaccine in uninfected human volunteers. Vaccine 2007;25:510-18
  • Mancini-Bourgine M, Fontaine H, Bréchot C, et al. Immunogenicity of a hepatitis B DNA vaccine administered to chronic HBV carriers. Vaccine 2006;24:4482-9
  • Nemunaitis J, Meyers T, Senzer N, et al. Phase I Trial of sequential administration of recombinant DNA and adenovirus expressing L523S protein in early stage non-small-cell lung cancer. Mol Ther 2006;13:1185-91
  • Purcell AW, McCluskey J, Rossjohn J. More than one reason to rethink the use of peptides in vaccine design. Nat Rev Drug Discov 2007;6:404-14
  • Chianese-Bullock KA, Irvin WP, Petroni GR, et al. A multipeptide vaccine is safe and elicits T-cell responses in participants with advanced stage ovarian cancer. J Immunother 2008;31:420-30
  • Kenter GG, Welters MJP, Valentijn ARPM, et al. Phase I immunotherapeutic trial with long peptides spanning the E6 and E7 sequences of high-risk human papillomavirus 16 in end-stage cervical cancer patients shows low toxicity and robust immunogenicity. Clin Cancer Res 2008;14:169-77
  • Slingluff CL, Petroni GR, Chianese-Bullock KA, et al. Immunologic and clinical outcomes of a randomized phase II trial of two multipeptide vaccines for melanoma in the adjuvant setting. Clin Cancer Res 2007;13:6386-95
  • Nielsen M, Lundegaard C, Lund O, et al. The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 2005;57:33-41
  • Sette A, Sidney J. Nine major HLA class I supertypes account for the vast preponderance of HLA-A and -B polymorphism. Immunogenetics 1999;50:201-12
  • Lund O, Nielsen M, Kesmir C, et al. Definition of supertypes for HLA molecules using clustering of specificity matrices. Immunogenetics 2004;55:797-810
  • Van Regenmortel MHV. Mapping epitope structure and activity: from one-dimensional prediction to four-dimensional description of antigenic specificity. Methods 1996;9:465-72
  • Greenbaum JA, Andersen PH, Blythe M, et al. Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools. J Mol Recognit 2007;20:75-82
  • Brockman AH, Orlando R, Tarleton RL. A new liquid chromatography/tandem mass spectrometric approach for the identification of class I major histocompatibility complex associated peptides that eliminates the need for bioassays. Rapid Commun Mass Spectrom 1999;13:1024-30
  • Schirle M, Keilholz W, Weber B, et al. Identification of tumor-associated MHC class I ligands by a novel T cell-independent approach. Eur J Immunol 2000;30:2216-25
  • Singh-Jasuja H, Emmerich NPN, Rammensee H-G. The Tübingen approach: identification, selection, and validation of tumor-associated HLA peptides for cancer therapy. Cancer Immunol Immunother 2004;53:187-95
  • Sette A, Vitiello A, Reherman B, et al. The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J Immunol 1994;153:5586-92
  • Lundegaard C, Lund O, Kesmir C, et al. Modeling the adaptive immune system: predictions and simulations. Bioinformatics 2007;23:3265-75
  • Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. New York: Springer, 2001
  • Ben-Hur A, Ong CS, Sonnenburg S, et al. Support vector machines and kernels for computational biology. PLoS Comput Biol 2008;4:e1000173
  • Baldi P, Brunak S. Bioinformatics: The Machine Learning Approach. Cambridge, MA: MIT Press, 2001
  • Rammensee H-G, Bachmann J, Emmerich NP, et al. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 1999;50:213-19
  • Brusic V, Rudy G, Harrison LC. MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res 1998;26:368-71
  • Bhasin M, Singh H, Raghava GPS. MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 2003;19:665-6
  • Toseland CP, Clayton DJ, McSparron H, et al. AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Res 2005;1:4
  • Peters B, Sidney J, Bourne P, et al. The immune epitope database and analysis resource: from vision to blueprint. PLoS Biol 2005;3:e91
  • Lefranc MP, Giudicelli V, Ginestoux C, et al. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res 2009;37:D1006-12
  • Robinson J, Waller MJ, Stoehr P, et al. IPD–the Immuno Polymorphism Database. Nucleic Acids Res 2005;33:D523-6
  • Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. Nucleic Acids Res 2000;28:235-42
  • Kaas Q, Ruiz M, Lefranc MP. IMGT/3Dstructure-DB and IMGT/StructuralQuery, a database and a tool for immunoglobulin, T cell receptor and MHC structural data. Nucleic Acids Res 2004;32:D208-10
  • Falk K, Rötzschke O, Stevanovic′ S, et al. Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 1991;351:290-6
  • Rötzschke O, Falk K, Stevanovic′ S, et al. Exact prediction of a natural T cell epitope. Eur J Immunol 1991;21:2891-4
  • Meister GE, Roberts CG, Berzofsky JA, et al. Two novel T cell epitope prediction algorithms based on MHC-binding motifs; comparison of predicted and published epitopes from Mycobacterium tuberculosis and HIV protein sequences. Vaccine 1995;13:581-91
  • 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;152:163-75
  • Stryhn A, Pedersen LO, Romme T, et al. 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;26:1911-18
  • Schafer JR, Jesdale BM, George JA, et al. Prediction of well-conserved HIV-1 ligands using a matrix-based algorithm, EpiMatrix. Vaccine 1998;16:1880-4
  • Reche PA, Glutting J-P, Reinherz EL. Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 2002;63:701-9
  • Nielsen M, Lundegaard C, Worning P, et al. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 2004;20:1388-97
  • Bui H-H, Sidney J, Peters B, et al. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 2005;57:304-14
  • 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;1:263-72
  • Peters B, Tong W, Sidney J, et al. Examining the independent binding assumption for binding of peptide epitopes to MHC-I molecules. Bioinformatics 2003;19:1765-72
  • Guan P, Hattotuwagama CK, Doytchinova IA, et al. MHCPred 2.0: an updated quantitative T-cell epitope prediction server. Appl Bioinformatics 2006;5:55-61
  • Guan P, Doytchinova IA, Zygouri C, et al. MHCPred: a server for quantitative prediction of peptide-MHC binding. Nucleic Acids Res 2003;31:3621-4
  • Hertz T, Yanover C. PepDist: a new framework for protein-peptide binding prediction based on learning peptide distance functions. BMC Bioinformatics 2006;7(Suppl 1):S3
  • Sung M-H, Simon R. Genomewide conserved epitope profiles of HIV-1 predicted by biophysical properties of MHC binding peptides. J Comput Biol 2004;11:125-45
  • Yanover C, Hertz T. Predicting protein-peptide binding affinity by learning peptide-peptide distance functions. in RECOMB. 2005
  • Trost B, Bickis M, Kusalik A. Strength in numbers: achieving greater accuracy in MHC-I binding prediction by combining the results from multiple prediction tools. Immunome Res 2007;3:5
  • Brusic V, Petrovsky N, Zhang G, et al. Prediction of promiscuous peptides that bind HLA class I molecules. Immunol Cell Biol 2002;80:280-5
  • Mamitsuka H. Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 1998;33:460-74
  • Udaka K, Mamitsuka H, Nakaseko Y, et al. Empirical evaluation of a dynamic experiment design method for prediction of MHC class I-binding peptides. J Immunol 2002;169:5744-53
  • Adams HP, Koziol JA. Prediction of binding to MHC class I molecules. J Immunol Methods 1995;185:181-90
  • Brusic V, Rudy G, Harrison LC. Prediction of MHC binding peptides using artificial neural networks. In: RJ Stonier, XH Yu, editors, Complex Systems: Mechanisms of Adaptation, IOS Press: Amsterdam; 1994. p. 253-60
  • Buus S, Lauemøller SL, Worning P, et al. Sensitive quantitative predictions of peptide-MHC binding by a ‘Query by Committee’ artificial neural network approach. Tissue Antigens 2003;62:378-84
  • Nielsen M, Lundegaard C, Worning P, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 2003;12:1007-17
  • Lundegaard C, Lund O, Nielsen M. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics 2008;24:1397-8
  • Dönnes P, Elofsson A. Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics 2002;3:25
  • Dönnes P, Kohlbacher O. SVMHC: a server for prediction of MHC-binding peptides. Nucleic Acids Res 2006;34:W194-7
  • Cui J, Han LY, Lin HH, et al. MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties. Immunogenetics 2006;58:607-13
  • Cui J, Han LY, Lin HH, et al. Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties. Mol Immunol 2007;44:866-77
  • Wan J, Liu W, Xu Q, et al. SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics 2006;7:463
  • Rognan D, Lauemoller SL, Holm A, et al. Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. J Med Chem 1999;42:4650-8
  • Schueler-Furman O, Altuvia Y, Sette A, et al. Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci 2000;9:1838-46
  • Jojic N, Reyes-Gomez M, Heckerman D, et al. Learning MHC I–peptide binding. Bioinformatics 2006;22:e227-35
  • Zhu S, Udaka K, Sidney J, et al. Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules. Bioinformatics 2006;22:1648-55
  • Heckerman D, Kadie C, Listgarten J. Leveraging information across HLA alleles/supertypes improves epitope prediction. J Comput Biol 2007;14:736-46
  • Sturniolo T, Bono E, Ding J, et al. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 1999;17:555-61
  • DeLuca DS, Khattab B, Blasczyk R. A modular concept of HLA for comprehensive peptide binding prediction. Immunogenetics 2007;59:25-35
  • Nielsen M, Lundegaard C, Blicher T, et al. 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
  • Hoof I, Peters B, Sidney J, et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 2008;61:1-13
  • Zhang H, Lund O, Nielsen M. The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Bioinformatics 2009;25:1293-9
  • Jacob L, Vert J-P. Efficient peptide-MHC-I binding prediction for alleles with few known binders. Bioinformatics 2008;24:358-66
  • Peters B, Bui H-H, Frankild S, et al. A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput Biol 2006;2:e65
  • Lin HH, Ray S, Tongchusak S, et al. Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol 2008;9:8
  • Zhang H, Lundegaard C, Nielsen M. Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods. Bioinformatics 2009;25:83-9
  • Larsen MV, Lundegaard C, Lamberth K, et al. An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions. Eur J Immunol 2005;35:2295-303
  • Eggers M, Boes-Fabian B, Ruppert T, et al. The cleavage preference of the proteasome governs the yield of antigenic peptides. J Exp Med 1995;182:1865-70
  • Holzhütter HG, Frömmel C, Kloetzel PM. A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20 S proteasome. J Mol Biol 1999;286:1251-65
  • Holzhütter HG, Kloetzel PM. A kinetic model of vertebrate 20S proteasome accounting for the generation of major proteolytic fragments from oligomeric peptide substrates. Biophys J 2000;79:1196-205
  • Mishto M, Luciani F, Holzhütter H-G, et al. Modeling the in vitro 20S proteasome activity: the effect of PA28-alphabeta and of the sequence and length of polypeptides on the degradation kinetics. J Mol Biol 2008;377:1607-17
  • Ginodi I, Vider-Shalit T, Tsaban L, et al. Precise score for the prediction of peptides cleaved by the proteasome. Bioinformatics 2008;24:477-83
  • Kuttler C, Nussbaum AK, Dick TP, et al. An algorithm for the prediction of proteasomal cleavages. J Mol Biol 2000;298:417-29
  • Nussbaum AK, Kuttler C, Hadeler KP, et al. PAProC: a prediction algorithm for proteasomal cleavages available on the WWW. Immunogenetics 2001;53:87-94
  • Keşmir C, Nussbaum AK, Schild H, et al. Prediction of proteasome cleavage motifs by neural networks. Protein Eng 2002;15:287-96
  • Dönnes P, Kohlbacher O. Integrated modeling of the major events in the MHC class I antigen processing pathway. Protein Sci 2005;14:2132-40
  • Bhasin M, Raghava GPS. Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences. Nucleic Acids Res 2005;33:W202-7
  • van Endert PM, Riganelli D, Greco G, et al. The peptide-binding motif for the human transporter associated with antigen processing. J Exp Med 1995;182:1883-95
  • 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;161:617-24
  • Brusic V, van Endert P, Zeleznikow J, et al. A neural network model approach to the study of human TAP transporter. In Silico Biol 1999;1:109-21
  • Zhang GL, Petrovsky N, Kwoh CK, et al. PRED(TAP): a system for prediction of peptide binding to the human transporter associated with antigen processing. Immunome Res 2006;2:3
  • Bhasin M, Raghava GPS. Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci 2004;13:596-607
  • Peters B, Bulik S, Tampe R, et al. Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. J Immunol 2003;171:1741-9
  • Kessler JH, Beekman NJ, Bres-Vloemans SA, et al. Efficient identification of novel HLA-A(*)0201-presented cytotoxic T lymphocyte epitopes in the widely expressed tumor antigen PRAME by proteasome-mediated digestion analysis. J Exp Med 2001;193:73-88
  • Hakenberg J, Nussbaum AK, Schild H, et al. MAPPP: MHC class I antigenic peptide processing prediction. Appl Bioinformatics 2003;2:155-8
  • Singh H, Raghava GPS. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics 2003;19:1009-14
  • Reche PA, Glutting J-P, Zhang H, et al. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics 2004;56:405-19
  • Bhasin M, Raghava GPS. A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes. J Biosci 2007;32:31-42
  • Larsen MV, Lundegaard C, Lamberth K, et al. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics 2007;8:424
  • Doytchinova IA, Guan P, Flower DR. EpiJen: a server for multistep T cell epitope prediction. BMC Bioinformatics 2006;7:131
  • Tenzer S, Peters B, Bulik S, et al. Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding. Cell Mol Life Sci 2005;62:1025-37
  • Bhasin M, Raghava GPS. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 2004;22:3195-204
  • Tung C-W, Ho S-Y. POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties. Bioinformatics 2007;23:942-9
  • Hammer J, Bono E, Gallazzi F, et al. Precise prediction of major histocompatibility complex class II-peptide interaction based on peptide side chain scanning. J Exp Med 1994;180:2353-8
  • Marshall KW, Wilson KJ, Liang J, et al. Prediction of peptide affinity to HLA DRB1*0401. J Immunol 1995;154:5927-33
  • Southwood S, Sidney J, Kondo A, et al. Several common HLA-DR types share largely overlapping peptide binding repertoires. J Immunol 1998;160:3363-73
  • Jung G, Fleckenstein B, von der Mülbe F, et al. From combinatorial libraries to MHC ligand motifs, T-cell superagonists and antagonists. Biologicals 2001;29:179-81
  • Brusic V, Rudy G, Honeyman G, et al. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 1998;14:121-30
  • Bhasin M, Raghava GPS. SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence. Bioinformatics 2004;20:421-3
  • Murugan N, Dai Y. Prediction of MHC class II binding peptides based on an iterative learning model. Immunome Res 2005;1:6
  • Wang P, Sidney J, Dow C, et al. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol 2008;4:e1000048
  • Mallios RR. Predicting class II MHC/peptide multi-level binding with an iterative stepwise discriminant analysis meta-algorithm. Bioinformatics 2001;17:942-8
  • Doytchinova IA, Flower DR. Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction. Bioinformatics 2003;19:2263-70
  • Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 2007;8:238
  • Noguchi H, Kato R, Hanai T, et al. Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. J Biosci Bioeng 2002;94:264-70
  • Kato R, Noguchi H, Honda H, et al. Hidden Markov model-based approach as the first screening of binding peptides that interact with MHC class II molecules. Enzyme Microbiol Technol 2003;33:472-81
  • Godkin AJ, Smith KJ, Willis A, et al. Naturally processed HLA class II peptides reveal highly conserved immunogenic flanking region sequence preferences that reflect antigen processing rather than peptide-MHC interactions. J Immunol 2001;166:6720-7
  • Singh H, Raghava GP. ProPred: prediction of HLA-DR binding sites. Bioinformatics 2001;17:1236-7
  • Nielsen M, Lundegaard C, Blicher T, et al. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Comput Biol 2008;4:e1000107
  • Davies MN, Sansom CE, Beazley C, et al. A novel predictive technique for the MHC class II peptide-binding interaction. Mol Med 2003;9:220-5
  • Zaitlen N, Reyes-Gomez M, Heckerman D, et al. Shift-invariant adaptive double threading: learning MHC II-peptide binding. J Comput Biol 2008;15:927-42
  • Lin HH, Zhang GL, Tongchusak S, et al. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics 2008;9(Suppl 12):S22
  • Feldhahn M, Thiel P, Schuler MM, et al. EpiToolKit–a web server for computational immunomics. Nucleic Acids Res 2008;36:W519-22
  • Schuler MM, Dönnes P, Nastke MD, et al. SNEP: SNP-derived epitope prediction program for minor H antigens. Immunogenetics 2005;57:816-20
  • Sette A, Fikes J. Epitope-based vaccines: an update on epitope identification, vaccine design and delivery. Curr Opin Immunol 2003;15:461-70
  • Gross D-A, Graff-Dubois S, Opolon P, et al. High vaccination efficiency of low-affinity epitopes in antitumor immunotherapy. J Clin Invest 2004;113:425-33
  • Dyall R, Bowne WB, Weber LW, et al. Heteroclitic immunization induces tumor immunity. J Exp Med 1998;188:1553-61
  • Tangri S, Ishioka GY, Huang X, et al. Structural features of peptide analogs of human histocompatibility leukocyte antigen class I epitopes that are more potent and immunogenic than wild-type peptide. J Exp Med 2001;194:833-46
  • Vertuani S, Sette A, Sidney J, et al. Improved immunogenicity of an immunodominant epitope of the HER-2/neu protooncogene by alterations of MHC contact residues. J Immunol 2004;172:3501-8
  • Stuge TB, Holmes SP, Saharan S, et al. Diversity and recognition efficiency of T cell responses to cancer. PLoS Med 2004;1:e28
  • Gnjatic S, Jäger E, Chen W, et al. CD⊕ T cell responses against a dominant cryptic HLA-A2 epitope after NY-ESO-1 peptide immunization of cancer patients. Proc Natl Acad Sci USA 2002;99:11813-18
  • Doytchinova IA, Walshe VA, Jones NA, et al. Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes. J Immunol 2004;172:7495-502
  • Bhasin M, Raghava GPS. Prediction of promiscuous and high-affinity mutated MHC binders. Hybrid Hybridomics 2003;22:229-34
  • Houghton CSB, Engelhorn ME, Liu C, et al. Immunological validation of the EpitOptimizer program for streamlined design of heteroclitic epitopes. Vaccine 2007;25:5330-42
  • Douat-Casassus C, Marchand-Geneste N, Diez E, et al. Synthetic anticancer vaccine candidates: rational design of antigenic peptide mimetics that activate tumor-specific T-cells. J Med Chem 2007;50:1598-609
  • Groot ASD, Marcon L, Bishop EA, et al. HIV vaccine development by computer assisted design: the GAIA vaccine. Vaccine 2005;23:2136-48
  • Vider-Shalit T, Raffaeli S, Louzoun Y. Virus-epitope vaccine design: informatic matching the HLA-I polymorphism to the virus genome. Mol Immunol 2007;44:1253-61
  • Toussaint NC, Dönnes 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
  • Toussaint NC, Kohlbacher O. OptiTope–a web server for the selection of an optimal set of peptides for epitope-based vaccines. Nucleic Acids Res 2009;37:W617-22
  • Fischer W, Perkins S, Theiler J, et al. Polyvalent vaccines for optimal coverage of potential T-cell epitopes in global HIV-1 variants. Nat Med 2007;13:100-6
  • Nickle DC, Rolland M, Jensen MA, et al. Coping with viral diversity in HIV vaccine design. PLoS Comput Biol 2007;3:e75
  • Kong W-P, Wu L, Wallstrom TC, et al. Expanded breadth of the T-cell response to mosaic human immunodeficiency virus type 1 envelope DNA vaccination. J Virol 2009;83:2201-15

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.