New Approaches to in silico Design of Epitope-Based Vaccines

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108 APPENDIX C. APPLICATIONS
Appendix D Contributions Chapter 4 — Epitope Discovery Section 4.2 — Improved Kernels for MHC Binding Prediction This work is part of a manuscript that has previously been published in BMC Bioinformatics [71]. It is joint work with Gunnar Rätsch (GR) and Oliver Kohlbacher (OK). NCT and GR conceived and designed the project. NCT prepared the data, implemented the kernels and performed experiments. GR supervised the project. OK contributed to the discussion. The contributions of Christian Widmer to the publication as well as GR’s implementations and experiments have not been incorporated in this thesis. Section 4.3 — MHC Binding Prediction for All MHC-I Alleles In addition to myself, Magdalena Feldhahn (MF), Oliver Kohlbacher (OK) and Matthias Ziehm (MZ) contributed to this work. OK conceived the project. NCT designed and performed the experiments. MZ retrieved the 3D structures and determined the pocket profiles. MF and OK contributed to the discussion. Section 4.4 — T-Cell Epitope Prediction In addition to myself, Magdalena Feldhahn (MF), Sebastian Briesemeister (SB), Matthias Ziehm (MZ), Gunnar Rätsch (GR), Stefan Stevanovic (SS) and Oliver Kohlbacher (OK) contributed to this project. NCT and OK designed the experiments. NCT performed the experiments and retrieved and preprocessed the data. SS provided experimental data. MZ retrieved the microarray data and determined the thymus proteomes. OK and MF implemented the distance tries. MF performed the distance-to-self calculations and the MHC binding predictions. MF, SB, GR and OK contributed to the discussion. Parts of this section were presented at the Second Immunoinformatics and Computational Immunology Workshop (ICIW 2011) and are included in the workshop proceedings [161]. 109
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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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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
Page 115 and 116: Table B.5: Overall performance of M
Page 117 and 118: Table B.7: Gene expression omnibus
Page 119: Appendix C Applications Table C.1:
Page 123 and 124: Appendix E Publications Published M
Page 125 and 126: Bibliography [1] M. Moutschen, P. L
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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