New Approaches to in silico Design of Epitope-Based Vaccines

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114 BIBLIOGRAPHY Patterson, R. Fink, and P. K. Rehm. Immunologic and clinical outcomes of a randomized phase II trial of two multipeptide vaccines for melanoma in the adjuvant setting. Clin Cancer Res, 13(21):6386–6395, 2007. [8] N. C. Toussaint and O. Kohlbacher. Towards in silico design of epitope-based vaccines. Expert Opin Drug Discovery, 4(10):1047–1060, 2009. [9] A. Sette, A. Vitiello, B. Reherman, P. Fowler, R. Nayersina, W. M. Kast, C. J. Melief, C. Oseroff, L. Yuan, J. Ruppert, J. Sidney, M. F. del Guercio, S. Southwood, R. T. Kubo, R. W. Chesnut, H. M. Grey, and F. V. Chisari. The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J Immunol, 153(12):5586–5592, 1994. [10] H. Singh-Jasuja, N. P. N. Emmerich, and H.-G. Rammensee. The Tübingen approach: identification, selection, and validation of tumor-associated HLA peptides for cancer therapy. Cancer Immunol Immun, 53(3):187–195, 2004. [11] K. C. Parker, M. A. Bednarek, and J. E. Coligan. Scheme for ranking potential HLA- A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol, 152(1):163–175, 1994. [12] H. Rammensee, J. Bachmann, N. P. N. Emmerich, O. A. Bachor, and S. Stevanović. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics, 50(3- 4):213–219, 1999. [13] P. Dönnes and A. Elofsson. Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinf, 3:25, 2002. [14] S. Buus, S. L. Lauemøller, P. Worning, C. Kesmir, T. Frimurer, S. Corbet, A. Fomsgaard, J. Hilden, A. Holm, and S. Brunak. Sensitive quantitative predictions of peptide-MHC binding by a ’Query by Committee’ artificial neural network approach. Tissue Antigens, 62(5):378–384, 2003. [15] J. Robinson, A. Malik, P. Parham, J. G. Bodmer, and S. G. Marsh. IMGT/HLA database–a sequence database for the human major histocompatibility complex. Tissue Antigens, 55(3):280–287, 2000. [16] J. Robinson, K. Mistry, H. McWilliam, R. Lopez, P. Parham, and S. G. Marsh. The IMGT/HLA database. Nucleic Acids Res, 39(Database issue):D1171–6, 2011. [17] J. Cui, L. Y. Han, H. H. Lin, Z. Q. Tang, L. Jiang, Z. W. Cao, and Y. Z. Chen. MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties. Immunogenetics, 58(8):607–613, 2006. [18] W. Liu, X. Meng, Q. Xu, D. R. Flower, and T. Li. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinf, 7:182, 2006.
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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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5.6. IMPLEMENTATION 61 number of no
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 and 124: Appendix E Publications Published M
Page 125: Bibliography [1] M. Moutschen, P. L
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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