New Approaches to in silico Design of Epitope-Based Vaccines

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52 CHAPTER 4. EPITOPE DISCOVERY
Chapter 5 Epitope Selection The previous chapter was concerned with the discovery of candidate epitopes from a given set of target antigens. In the following, we will concentrate on the subsequent step in the EV design pipeline: epitope selection (Figure 5.1). Given a set of candidate epitopes, the task is to select the set of epitopes which yields the best immune response in the target population. Parts of this chapter have previously been published [8, 102, 103]. 5.1 Introduction Epitope selection is a critical step in the design of an EV. Regulatory and economic issues impose a strong limitation on the number of epitopes that can be included in an EV. From a set of target antigens, candidate epitopes can be determined either experimentally or, as proposed in the previous chapter, computationally. The crucial task is to select a set of epitopes from these candidate epitopes that yields the best immune response in the target population while at the same time keeping the number of epitopes low. The selection is usually made on a case-by-case basis considering properties such as overall immunogenicity, mutation tolerance, population coverage, coverage of antigens, antigen processing as well as properties of the source antigen. The selection methods used by the pharmaceutical industry are manifold. Figure 5.1: EV design: epitope selection step. Out of the set of candidate epitopes determined via epitope discovery, the most suitable subset for use in the EV has to be selected. Figure based on [8]. 53
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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
Page 13 and 14: Chapter 1 Introduction Motivation T
Page 15 and 16: prediction of T-cell epitopes is a
Page 17 and 18: systemic property but employ peptid
Page 19 and 20: Chapter 2 Biological Background In
Page 21 and 22: 2.2. CELLULAR IMMUNE RESPONSE 9 A B
Page 23 and 24: 2.2. CELLULAR IMMUNE RESPONSE 11 [3
Page 25 and 26: 2.2. CELLULAR IMMUNE RESPONSE 13 Ho
Page 27 and 28: 2.3. VACCINES 15 the immune system
Page 29 and 30: Chapter 3 Algorithmic Background In
Page 31 and 32: 3.1. COMBINATORIAL OPTIMIZATION 19
Page 33 and 34: 3.2. MACHINE LEARNING 21 Figure 3.2
Page 41 and 42: Chapter 4 Epitope Discovery After h
Page 43 and 44: 4.1. INTRODUCTION 31 Figure 4.2: Sc
Page 45 and 46: 4.2. IMPROVED KERNELS FOR MHC BINDI
Page 51 and 52: 4.3. MHC BINDING PREDICTION FOR ALL
Page 57 and 58: 4.4. T-CELL EPITOPE PREDICTION 45 T
Page 59 and 60: 4.4. T-CELL EPITOPE PREDICTION 47 i
Page 61 and 62: 4.4. T-CELL EPITOPE PREDICTION 49 F
Page 63: 4.4. T-CELL EPITOPE PREDICTION 51 t
Page 67 and 68: 5.3. MATHEMATICAL ABSTRACTION 55 im
Page 69 and 70: 5.3. MATHEMATICAL ABSTRACTION 57 fo
Page 71 and 72: 5.4. EXPERIMENTAL RESULTS 59 Let G
Page 73 and 74: 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
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Table B.5: Overall performance of M
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Table B.7: Gene expression omnibus
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Appendix C Applications Table C.1:
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Appendix D Contributions Chapter 4
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Appendix E Publications Published M
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Bibliography [1] M. Moutschen, P. L
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BIBLIOGRAPHY 115 [19] T. Sturniolo,
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BIBLIOGRAPHY 117 [44] F. Morein and
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BIBLIOGRAPHY 119 [72] C. Widmer, N.
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BIBLIOGRAPHY 121 [97] H. Parkinson,
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BIBLIOGRAPHY 123 [123] NCBI dbMHC d
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BIBLIOGRAPHY 125 [150] World Health
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