New Approaches to in silico Design of Epitope-Based Vaccines

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44 CHAPTER 4. EPITOPE DISCOVERY predictions. Incorporation of related data set sizes promises to improve the confidence measure. NetMHCpan yields impressive performances for the majority of the IEDB h9 alleles, seen and unseen. It is the best pan-specific MHC-I binding predictor available. What is the secret to the success of NetMHCpan? According to the authors it is the way the data is represented. However, an attempt to measure up to its performance by reimplementing the NetMHCpan approach using SVMs was not successful. Assuming that Nielsen et al. thoroughly described their approach and that we correctly reimplemented it, two possible explanations remain: (a) we either chose unfavorable parameter ranges for the grid search and the secret lies in the data representation after all or (b) artificial neural networks are simply better suited for the pan-specific modelling of pMHC-I binding. A more detailed analysis would be required to come to a well-founded conclusion regarding the secret to the success of NetMHCpan. From a problem-oriented point of view, however, it is far more expedient to focus on optimally exploiting the superior performances of NetMHCpan and its allele-specific counterpart NetMHC [75] for EV design. 4.4 T-Cell Epitope Prediction 4.4.1 Introduction So far, we have only considered pMHC binding. However, induction of an immune response requires recognition of a pMHC complex by a host T cell. Complex dependencies and an incomplete biological knowledge make the prediction of T-cell epitopes a very challenging problem. The more recent machine-learning based approaches employ a sparse encoding [21], a physicochemical encoding (POPI by Tung et al. [22]) or a WD kernel (POPISK by Tung et al., unpublished work) to train SVMs on peptide sequences. Common to all of the approaches is their limited prediction accuracy. Reasons for this can be found in the choice of training data and in the methods’ limited view on the problem. Taking a wider view on T-cell reactivity, we propose to incorporate knowledge on the immune system’s self-tolerance into the prediction: Selection processes in the thymus and in the periphery eliminate T cells capable of reacting against self-peptides. Thus, antigenic peptides that are very similar to self-peptides are highly unlikely to induce a T-cell response. We present an approach to T-cell epitope prediction that combines sequence information and information on self-tolerance. Based on a carefully designed data set comprising T-cell reactivity data in the context of HLA-B*35:01, we can show that our predictor outperforms purely sequence-based predictors, demonstrating the validity of our approach. 4.4.2 Modelling Self-Tolerance Self-tolerance describes the capability of the immune system to distinguish self from nonself. A key mechanism for ensuring self-tolerance is negative T-cell selection. Negative T-cell selection eliminates or inactivates self-reactive T cells, rendering antigenic peptides that are very similar to self-peptides highly unlikely to induce a T-cell response. Thus, in order to model self-tolerance, we require a representative set of self-peptides presented to
4.4. T-CELL EPITOPE PREDICTION 45 T cells during negative selection. Since only MHC binding peptides are presented, MHC binding affinity should be taken into account. Furthermore, an adequate similarity measure for peptides is needed. For the sake of convenience, we will use a distance measure instead of a similarity measure. Reference Proteome Since negative T-cell selection takes place in the thymus, the thymus proteome represents a reasonable reference set for central tolerance. However, this set is too small to also model peripheral tolerance: in this case the complete host proteome has to be considered. We generate peptide reference sets based on the thymus proteome, modelling central tolerance, as well as on the human proteome, modelling both central and peripheral tolerance. Only peptides binding to the MHC molecule under consideration can be employed for T-cell selection. Hence, only peptides predicted to be MHC binders (IC50 ≤ 500) by the state-of-the-art MHC-I binding predictor NetMHC [14, 75] (version 3.0) are included in the peptide reference sets. Since the majority of all known MHC-I ligands are of length nine, we will only consider ninemers in the following. Human proteome. The general human proteome was retrieved from the International Protein Index (hereafter IPI) [95] (version 3.47). Thymus proteome. We used gene expression data to define the thymus proteome. Whole genome microarray data was downloaded from the NCBI Gene Expression Omnibus [96] and from the EBI ArrayExpress database [97]. The employed microarrays only cover about 43% of all proteins present in the IPI human proteome. Thus, we cannot make a statement regarding the expression in the thymus of the remaining 57%. Due to conflicting measurements regarding the presence of proteins in the thymus, we employ a majority voting to unambiguously assign each protein to one of the three classes: present, marginally expressed, and absent. For details on the employed data sets as well as on the majority voting, please refer to Tables B.7 and B.8 and to Algorithm B.1 in the appendix. According to the majority voting, approximately 45% of the covered proteins are present in the thymus, 26% are marginally expressed, and 28% are absent from the thymus. Given these three groups of proteins, we define a minimum thymus proteome (hereafter thymus-min) consisting of all proteins in the present group and a maximum thymus proteome (hereafter thymus-max) comprising present and marginally expressed proteins. Binding Affinities Not every self-peptide contributes to self-tolerance. Crucial features are pMHC binding as well as, according to the affinity model, pMHC:TCR affinity. We account for the aspect of pMHC binding by including only self-peptides predicted to bind to the respective MHC molecule in the reference peptide set. Regarding the pMHC:TCR affinity, there is no accurate prediction method available. However, the stability of the ternary complex pMHC:TCR is affected by pMHC affinity, which can be predicted accurately. Thus, we
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New Approaches to in silico Design
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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 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
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Page 65 and 66: Chapter 5 Epitope Selection The pre
Page 67 and 68: 5.3. MATHEMATICAL ABSTRACTION 55 im
Page 69 and 70: 5.3. MATHEMATICAL ABSTRACTION 57 fo
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
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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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