New Approaches to in silico Design of Epitope-Based Vaccines

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In accordance with the standard scientific protocol, I will use the personal pronoun we to indicate the reader and the writer, or my scientific collaborators and myself. viii
Contents 1 Introduction 1 2 Biological Background 7 2.1 The Immune System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.2 Adaptive Immunity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Cellular Immune Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2 The Major Histocompatibility Complex . . . . . . . . . . . . . . . . 8 2.2.3 Antigen Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.4 T Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.5 Experimental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 Vaccines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.2 Epitope-Based Vaccines . . . . . . . . . . . . . . . . . . . . . . . . . 14 3 Algorithmic Background 17 3.1 Combinatorial Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.1.2 Integer Linear Programs . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2.1 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2.2 Model Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4 Epitope Discovery 29 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Improved Kernels for MHC Binding Prediction . . . . . . . . . . . . . . . . 32 4.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.3 MHC Binding Prediction for All MHC-I Alleles . . . . . . . . . . . . . . . . 38 4.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 ix
Page 1 and 2: New Approaches to in silico Design
Page 3: Abstract Traditional trial-and-erro
Page 7: Acknowledgments First of all, I wou
Page 11 and 12: 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 and 64:
4.4. T-CELL EPITOPE PREDICTION 51 t
Page 65 and 66:
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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