New Approaches to in silico Design of Epitope-Based Vaccines

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4.3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.4 T-Cell Epitope Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.4.2 Modelling Self-Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.4.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5 Epitope Selection 53 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3 Mathematical Abstraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.3.1 ILP Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.3.2 Non-Linear Requirements . . . . . . . . . . . . . . . . . . . . . . . . 58 5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.5 Problem Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.6 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6 Epitope Assembly 63 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.2.1 ILP Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.2.2 Heuristic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 6.3 Incorporation of Proteasomal Cleavage Predictions . . . . . . . . . . . . . . 67 6.3.1 Cleavage Site Predictions . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3.2 Cleaved Fragment Predictions . . . . . . . . . . . . . . . . . . . . . . 67 6.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.4.1 Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.4.2 Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 7 Applications 73 7.1 OptiTope – A Web Server for Epitope Selection . . . . . . . . . . . . . . . . 73 7.1.1 Web Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 7.1.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 7.1.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 7.2 Design of a Peptide Cocktail Vaccine . . . . . . . . . . . . . . . . . . . . . . 77 7.2.1 Materials & Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 7.2.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 78 7.2.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 x
7.3 Design of String-of-Beads Vaccines . . . . . . . . . . . . . . . . . . . . . . . 82 7.3.1 Materials & Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 7.3.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.3.3 Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 7.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 8 Discussion & Conclusion 93 A Abbreviations 97 B Epitope Discovery 99 C Applications 107 D Contributions 109 E Publications 111 xi
Page 1 and 2: New Approaches to in silico Design
Page 3: Abstract Traditional trial-and-erro
Page 7 and 8: Acknowledgments First of all, I wou
Page 9: Contents 1 Introduction 1 2 Biologi
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
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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
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Chapter 6 Epitope Assembly The math
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6.2. APPROACH 65 Figure 6.2: Graph
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6.3. INCORPORATION OF PROTEASOMAL C
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6.4. EXPERIMENTAL RESULTS 69 Figure
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6.5. DISCUSSION 71 epitope sets wit
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Chapter 7 Applications In the previ
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7.1. OPTITOPE - A WEB SERVER FOR EP
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7.2. DESIGN OF A PEPTIDE COCKTAIL V
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7.3. DESIGN OF STRING-OF-BEADS VACC
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Chapter 8 Discussion & Conclusion P
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selection of an optimal epitope set
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Appendix A Abbreviations AA Amino a
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Appendix B Epitope Discovery Table
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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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New Approaches to in silico Design of Epitope-Based Vaccines

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