New Approaches to in silico Design of Epitope-Based Vaccines

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8 CHAPTER 2. BIOLOGICAL BACKGROUND cyte carries several thousand copies of the same receptor. Due to a genetic recombination mechanism, the genes encoding for the antigen receptor can generate 10 9 to 10 10 different specificities. Despite this vast amount of different receptors the adaptive immune system generally does not attack self molecules but very selectively attacks foreign substances. Selection processes that eliminate lymphocytes carrying receptors recognizing self are responsible for this self-tolerance. Once a lymphocyte recognizes an epitope, it proliferates and differentiates into effector cells and memory cells. The short-lived effector cells are engaged in the elimination of the antigen, while the long-lived memory cells mediate the immunological memory and respond rapidly on reexposure to the same antigen. The antigen receptors of B cells are membrane-bound antibodies. They recognize epitopes of antigen that is free in solution, ranging from bacteria to soluble proteins and polysaccharides. On recognition, B cells differentiate into antibody-secreting plasma cells. The secreted antibodies bind to antigen and thereby neutralize it or coat it facilitating elimination by cells of the innate immune system. The adaptive immune response induced by B cells is called humoral immune response. The antigen receptors of T cells only exist in a membrane-bound form. These T-cell receptors (TCRs) are structurally similar to antibodies. However, they are only capable of recognizing antigenic peptides bound to specific cell-membrane proteins called major histocompatibility complex (MHC) molecules. The adaptive immune response induced by T cells is called cellular or cell-mediated immune response. 2.2 Cellular Immune Response Vaccines make use of the the humoral as well as the cellular part of the adaptive immune response. The focus of this thesis is on the cellular immune response, which we will describe in more detail here. 2.2.1 Introduction Key players in the cellular immune response are MHC molecules and TCRs. MHC molecules present peptides on the cell surface for surveillance by the immune system. These peptides are derived from intracellular and extracellular proteins via a process called antigen processing. Migrating T cells, via their TCRs, scan peptide:MHC (pMHC) complexes on the surfaces of other cells. On recognition of an epitope bound to an MHC molecule, a T-cell response is induced. The type of response depends on the respective T cell: cytotoxic T cells (CTLs) kill infected and abnormal self-cells, helper T cells activate other cells of the innate and adaptive immune system. A peptide that is capable of inducing a T cellmediated immune response when bound to a specific allelic variant of MHC molecules is said to be immunogenic with respect to the corresponding MHC allele. 2.2.2 The Major Histocompatibility Complex The major histocompatibility complex (MHC) is a large cluster of genes in jawed vertebrates. Within this region of the genome the MHC class I (MHC-I) and MHC class II (MHC-II)
2.2. CELLULAR IMMUNE RESPONSE 9 A B C D Figure 2.1: Peptides bound to MHC-I and -II binding grooves. A) 3D structure of a 10-mer bound to an MHC-I molecule (PDB-ID: 1JF1). B) 3D structure of a 15-mer bound to an MHC-II molecule (PDB-ID: 1BX2). The binding groove is colored gray. C) Sketch of a ninemer bound to an MHC-I binding groove. D) Sketch of a 13-mer bound to an MHC-II binding groove. AAs protruding out of the binding groove are colored dark green. (A and B were visualized with BALLView [29]. C and D are based on [8].) genes are located. They encode for the α-chain of MHC-I and for the α- and β-chains of MHC-II molecules. In addition, the MHC comprises various other genes coding for proteins involved in antigen processing. In humans, this complex is called human leukocyte antigen (HLA) complex. The MHC is polygenic: it contains several different MHC-I and MHC-II genes. In humans, there are three MHC-I α-chain genes (HLA-A, HLA-B, HLA-C) and three pairs of genes encoding for the MHC-II α- and β-chains (HLA-DR, HLA-DP, HLA-DQ). Furthermore, the MHC genes are highly polymorphic. As of today, 4,946 MHC-I and 1,457 MHC-II alleles are known. They encode for 3,647 different MHC-I and 1,073 different MHC-II molecules, respectively (IMGT/HLA database [15, 16], release 3.4.0). The products of the MHC-I and MHC-II genes bind peptides in a peptide-binding groove (Figure 2.1A,B) and present them to T cells. The binding groove is formed by a large flat β-sheet enclosed by two α-helices. Its amino acid (AA) sequence varies between products of different MHC alleles. Thus, different allelic variants of MHC molecules bind different sets of peptides. Ligands of a particular MHC molecule display an allele-specific binding motif with a small number of highly conserved positions, the so-called anchors. Groups of MHC allelic variants with similar binding specificities are referred to as MHC supertypes [28]. Despite these similarities, MHC-I and MHC-II molecules have different functions.
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 and 10: Contents 1 Introduction 1 2 Biologi
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: Chapter 2 Biological Background In
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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Page 63 and 64: 4.4. T-CELL EPITOPE PREDICTION 51 t
Page 65 and 66: Chapter 5 Epitope Selection The pre
Page 67 and 68: 5.3. MATHEMATICAL ABSTRACTION 55 im
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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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