New Approaches to in silico Design of Epitope-Based Vaccines

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48 CHAPTER 4. EPITOPE DISCOVERY Figure 4.7: Data set composition. The distribution of NetMHC scores in the immunogenic and non-immunogenic examples of (A) the original set of EBV ninemers, D, and (B) the subset selected for training, ˜ D, is displayed. reported within this subsection are averaged over 100 runs of two-times nested five-fold cross validation to reduce random fluctuations of the performance. Sequence-Based Predictions For the prediction of T-cell epitopes based on sequence information only, we use SVC with a Gaussian RBF kernel on blosum50-encoded peptide sequences. The mean auROC is 0.72. As a comparison, an approach based on POPI, i.e., SVC with a Gaussian RBF kernel on physicochemically encoded peptide sequences, achieves a mean auROC of 0.69. Employment of a WD kernel as in POPISK yields a mean auROC of 0.70. Incorporation of Self-Tolerance Incorporation of self-tolerance is achieved by adding sequence-based and self-tolerancebased kernels. The self-tolerance-based kernel is a Gaussian RBF kernel on the 201dimensional feature vectors encoding self-tolerance information with respect to one of the three reference proteomes: thymus-min, thymus-max or IPI. While incorporation of the thymus-max-based self-tolerance information resulted in a considerable improvement (auROC = 0.78), this was not the case for the IPI- and thymus-min-based self-tolerance information (auROCs of 0.70 and 0.71, respectively). This is consistent with the performance of purely self-tolerance-based predictors: the thymus-max-based predictor (auROC = 0.58) clearly outperforms the IPI- (auROC = 0.48) and thymus-min-based (auROC = 0.44) predictors.
4.4. T-CELL EPITOPE PREDICTION 49 Figure 4.8: Performances of sequence-based and combined T-cell epitope prediction models. The mean ROC curve over 100 runs of two-times nested five-fold cross validation is displayed for three sequence-based predictors and for a predictor combining sequence and selftolerance information. The sequence-based predictors are (a) blosum50: a Gaussian RBF kernel with blosum50 encoding, (b) POPI-based: a Gaussian RBF kernel with the physicochemical encoding proposed in [22], and (c) POPISK-based: a WD kernel. The combined predictor is based on a blosum50-encoding of the peptide sequences and a thymus-max-based self-tolerance model. Additionally, the average performance of POPI [22, 99] is given (+). Comparison to Previously Proposed Approaches We compare our combined model to the non-allele-specific predictor POPI as well as to HLA-B*35:01-specific reimplementations of the POPI and POPISK approaches. A comparison to the prediction method proposed in [21] is omitted due to the user-unfriendly web server. Since this method also disregards MHC allele information, it can safely be assumed not to perform significantly better than POPI. The performances of the individual methods are displayed in Figure 4.8. We retrieved POPI predictions for the peptides in our data set from the POPI web server [99]. POPI assigns a peptide to one of four immunogenicity classes: none, little, moderate and high. In order to compare the performance of POPI to our prediction models, the four classes need to be mapped to the two classes immunogenic and non-immunogenic. We consider all classes except for none as immunogenic. Using this mapping and the same splits as above, we determine the mean sensitivity and specificity of POPI. From Figure 4.8 it can clearly be seen that our allele-specific T-cell epitope predictors significantly outperform the non-specific POPI. The negative effect of disregarding allele information when training immunogenicity predictors becomes obvious, especially when considering the performance of our allele-specific reimplementation of POPI. Since POPISK is a predictor for T-cell epitopes in the context of the HLA-A2 supertype,
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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
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
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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
Page 107 and 108: selection of an optimal epitope set
Page 109 and 110: 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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