New Approaches to in silico Design of Epitope-Based Vaccines

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110 APPENDIX D. CONTRIBUTIONS Chapter 5 — Epitope Selection This work is part of a manuscript that has previously been published in PLoS Computational Biology [102]. It is joint work with Pierre Dönnes (PD) and Oliver Kohlbacher (OK). NCT, PD and OK conceived and designed the experiments. NCT performed the experiments. NCT and OK analyzed the data. Chapter 6 — Epitope Assembly This work is part of a manuscript that has previously been published in Vaccine [162]. It is joint work with Yaakov Maman (YM), Oliver Kohlbacher (OK) and Yoram Louzoun (YL). The contributions of YM have not been incorporated in this chapter. NCT and YL conceived and designed the experiments. NCT conceived the algorithm and performed the experiments. OK contributed to the discussion. Chapter 7 — Applications Section 7.1 — OptiTope – A Web Server for Epitope Selection This work has previously been published in the web server issue of Nucleic Acids Research [103]. It is joint work with Oliver Kohlbacher (OK). NCT and OK designed the structure of the web server. NCT implemented the web server. Section 7.2 — Design of a Peptide Cocktail Vaccine This work is part of a manuscript that has previously been published in PLoS Computational Biology [102]. It is joint work with Pierre Dönnes (PD) and Oliver Kohlbacher (OK). NCT, PD and OK conceived and designed the experiments. NCT performed the experiments. NCT and OK analyzed the data. Section 7.3 — Design of String-of-Beads Vaccines This work is part of a manuscript that has previously been published in Vaccine [162]. It is joint work with Yaakov Maman (YM), Oliver Kohlbacher (OK) and Yoram Louzoun (YL). NCT and YL conceived and designed the experiments. NCT performed the experiments. OK contributed to the discussion. YM retrieved the influenza sequences. YL and YM performed the biological analysis.
Appendix E Publications Published Manuscripts • N. C. Toussaint, P. Dönnes, and O. Kohlbacher. A mathematical framework for the selection of an optimal set of peptides for epitope-based vaccines. PLoS Computational Biology, 4(12):e1000246, 2008. – Text and figures from this manuscript appear in Chapters 1 & 5 and in Section 7.2 of this thesis. • N. C. Toussaint and O. Kohlbacher. Towards in silico design of epitope-based vaccines. Expert Opinion on Drug Discovery, 4(10):1047-1060, 2009. – Text and figures from this manuscript appear throughout this thesis. • N. C. Toussaint and O. Kohlbacher. OptiTope – a web server for the selection of an optimal set of peptides for epitope-based vaccines. Nucleic Acids Research, 37:W617-22, 2009. – Text and figures from this manuscript appear in Section 7.1 of this thesis. • N. C. Toussaint, C. Widmer, O. Kohlbacher, and G. Rätsch. Exploiting physicochemical properties in string kernels. BMC Bioinformatics, 11(Suppl 8):S7, 2010. – Text and figures from this manuscript appear in Chapters 1 & 3 and in Section 4.2 of this thesis. • C. Widmer*, N. C. Toussaint*, Y. Altun, O. Kohlbacher, and G. Rätsch. Novel machine learning methods for MHC class I binding prediction. In: Proceedings of PRIB 2010, Lecture Notes in Computer Sciences, 2010. *) Joint first author. • C. Widmer, Y. Altun, N. C. Toussaint, and G. Rätsch. Inferring latent task structure for multi-task learning via multiple kernel learning. BMC Bioinformatics, 11(Suppl 8):S5, 2010. 111
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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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Page 75 and 76: Chapter 6 Epitope Assembly The math
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Page 83 and 84: 6.5. DISCUSSION 71 epitope sets wit
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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: Appendix D Contributions Chapter 4
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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