New Approaches to in silico Design of Epitope-Based Vaccines

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112 APPENDIX E. PUBLICATIONS • A. Hildebrandt, A. K. Dehof, A. Rurainski, A. Bertsch, M. Schumann, N. C. Toussaint, A. Moll, D. Stöckel, S. Nickels, S. C. Mueller, H.-P. Lenhof, and O. Kohlbacher. BALL - Biochemical algorithms library 1.3. BMC Bioinformatics, 11:531, 2010. • S. Canzar, N. C. Toussaint, and G. W. Klau. An exact algorithm for side-chain placement in protein design. Optimization Letters, 1-14, 2011. • N. C. Toussaint, M. Feldhahn, M. Ziehm, S. Stevanović, and O. Kohlbacher. T-cell epitope prediction based on self-tolerance. In: Proceedings of the Second Immunoinformatics and Computational Immunology Workshop, 2011. – Text and figures from this manuscript appear in Chapter 1 and in Section 4.4 of this thesis. • N. C. Toussaint, Y. Maman, O. Kohlbacher, and Y. Louzoun. Universal peptide vaccines – optimal peptide vaccine design based on viral sequence conservation. Vaccine, 2011 Aug 27 [Epub ahead of print]. – Text and figures from this manuscript appear in Chapters 1 & 6 and in Section 7.3 of this thesis.
Bibliography [1] M. Moutschen, P. Lonard, E.M. Sokal, F. Smets, M. Haumont, P. Mazzu, A. Bollen, F. Denamur, P. Peeters, G. Dubin, and M. Denis. Phase I/II studies to evaluate safety and immunogenicity of a recombinant gp350 Epstein-Barr virus vaccine in healthy adults. Vaccine, 25(24):4697–4705, 2007. [2] P. A. Goepfert, G. D. Tomaras, H. Horton, D. Montefiori, G. Ferrari, M. Deers, G. Voss, M. Koutsoukos, L. Pedneault, P. Vandepapeliere, M. J. McElrath, P. Spearman, J. D Fuchs, B. A. Koblin, W. A. Blattner, S. Frey, L. R. Baden, Clayton Harro, Thomas Evans, and NIAID HIV Vaccine Trials Network. Durable HIV-1 antibody and T-cell responses elicited by an adjuvanted multi-protein recombinant vaccine in uninfected human volunteers. Vaccine, 25(3):510–518, 2007. [3] M. Mancini-Bourgine, H. Fontaine, C. Bréchot, S. Pol, and M.-L. Michel. Immunogenicity of a hepatitis B DNA vaccine administered to chronic HBV carriers. Vaccine, 24(21):4482–4489, 2006. [4] J. Nemunaitis, T. Meyers, N. Senzer, C. Cunningham, Howard West, Eric Vallieres, S. Anthony, S. Vukelja, B. Berman, H. Tully, B. Pappen, S. Sarmiento, R. Arzaga, S. Duniho, S. Engardt, M. Meagher, and M. A. Cheever. Phase I trial of sequential administration of recombinant DNA and adenovirus expressing L523S protein in early stage non-small-cell lung cancer. Mol Ther, 13(6):1185–1191, 2006. [5] K. A. Chianese-Bullock, W. P. Irvin, G. R. Petroni, C. Murphy, M. Smolkin, W. C. Olson, E. Coleman, S. A. Boerner, C. J. Nail, P. Y. Neese, A. Yuan, K. T. Hogan, and C. L. Slingluff. A multipeptide vaccine is safe and elicits T-cell responses in participants with advanced stage ovarian cancer. J Immunother, 31(4):420–430, 2008. [6] G. G. Kenter, M. J. P. Welters, A. R. P. M. Valentijn, M. J. G. Lowik, D. M. A. Berends van der Meer, A. P. G. Vloon, J. W. Drijfhout, A. R. Wafelman, J. Oostendorp, G. J. Fleuren, R. Offringa, S. H. van der Burg, and C. J. M. Melief. 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, 14(1):169–177, 2008. [7] C. L. Slingluff, G. R. Petroni, K. A. Chianese-Bullock, M. E. Smolkin, S. Hibbitts, C. Murphy, N. Johansen, W. W. Grosh, G. V. Yamshchikov, P. Y. Neese, J. W. 113
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New Approaches to in silico Design
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Abstract Traditional trial-and-erro
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Acknowledgments First of all, I wou
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Contents 1 Introduction 1 2 Biologi
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7.3 Design of String-of-Beads Vacci
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Chapter 1 Introduction Motivation T
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prediction of T-cell epitopes is a
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systemic property but employ peptid
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Chapter 2 Biological Background In
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2.2. CELLULAR IMMUNE RESPONSE 9 A B
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2.2. CELLULAR IMMUNE RESPONSE 11 [3
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2.2. CELLULAR IMMUNE RESPONSE 13 Ho
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2.3. VACCINES 15 the immune system
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Chapter 3 Algorithmic Background In
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3.1. COMBINATORIAL OPTIMIZATION 19
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3.2. MACHINE LEARNING 21 Figure 3.2
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Chapter 4 Epitope Discovery After h
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4.1. INTRODUCTION 31 Figure 4.2: Sc
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4.2. IMPROVED KERNELS FOR MHC BINDI
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4.3. MHC BINDING PREDICTION FOR ALL
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4.4. T-CELL EPITOPE PREDICTION 45 T
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4.4. T-CELL EPITOPE PREDICTION 47 i
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4.4. T-CELL EPITOPE PREDICTION 49 F
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4.4. T-CELL EPITOPE PREDICTION 51 t
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Chapter 5 Epitope Selection The pre
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5.3. MATHEMATICAL ABSTRACTION 55 im
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5.3. MATHEMATICAL ABSTRACTION 57 fo
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5.4. EXPERIMENTAL RESULTS 59 Let G
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Page 75 and 76: Chapter 6 Epitope Assembly The math
Page 77 and 78: 6.2. APPROACH 65 Figure 6.2: Graph
Page 79 and 80: 6.3. INCORPORATION OF PROTEASOMAL C
Page 81 and 82: 6.4. EXPERIMENTAL RESULTS 69 Figure
Page 83 and 84: 6.5. DISCUSSION 71 epitope sets wit
Page 85 and 86: Chapter 7 Applications In the previ
Page 87 and 88: 7.1. OPTITOPE - A WEB SERVER FOR EP
Page 89 and 90: 7.2. DESIGN OF A PEPTIDE COCKTAIL V
Page 95 and 96: 7.3. DESIGN OF STRING-OF-BEADS VACC
Page 105 and 106: Chapter 8 Discussion & Conclusion P
Page 107 and 108: selection of an optimal epitope set
Page 109 and 110: Appendix A Abbreviations AA Amino a
Page 111 and 112: Appendix B Epitope Discovery Table
Page 113 and 114: Table B.3: Regression performances
Page 115 and 116: Table B.5: Overall performance of M
Page 117 and 118: Table B.7: Gene expression omnibus
Page 119 and 120: Appendix C Applications Table C.1:
Page 121 and 122: Appendix D Contributions Chapter 4
Page 123: Appendix E Publications Published M
Page 127 and 128: BIBLIOGRAPHY 115 [19] T. Sturniolo,
Page 129 and 130: BIBLIOGRAPHY 117 [44] F. Morein and
Page 131 and 132: BIBLIOGRAPHY 119 [72] C. Widmer, N.
Page 133 and 134: BIBLIOGRAPHY 121 [97] H. Parkinson,
Page 135 and 136: BIBLIOGRAPHY 123 [123] NCBI dbMHC d
Page 137 and 138: BIBLIOGRAPHY 125 [150] World Health
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New Approaches to in silico Design of Epitope-Based Vaccines

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