New Approaches to in silico Design of Epitope-Based Vaccines

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40 CHAPTER 4. EPITOPE DISCOVERY Figure 4.5: Pocket profiles. The columns give the MHC residue numbering. The rows correspond to the peptide positions. Gray squares mark interactions of the respective MHC residue with the respective peptide residue. MHC residues interacting with peptide position 1 are assigned to pocket 1 and so on. the corresponding pMHC binding affinities an SVR model with a Gaussian RBF kernel is trained. 4.3.3 Experimental Results UniTope is designed to allow predictions for all known MHC-I alleles whether they were included in the training set or not. MHC alleles which were included in the training set are termed seen, those not included are termed unseen alleles. In order to assess the performance of UniTope a comparison to other prediction methods is expedient. Here we are faced with a common problem in comparing prediction methods: a fair comparison between two methods is only given if both methods employed the same data for training and, if considering cross validation results, the same data splits. To facilitate the evaluation of MHC-I binding prediction methods, Peters et al. published the IEDB benchmark data set comprising quantitative pMHC-I affinity measurements (IC50 values) relating to various human, mouse, macaque, and chimpanzee MHC-I alleles [86]. Along with this data set, the authors established a set of benchmark predictions with three prediction methods: a neural network and two matrix-based approaches. All methods yield quantitative predictions based on allele-specific binding data only. The neural network approach (hereafter ANN) [75] outperforms the matrix-based approaches. Thus, in the following, we will compare the performance of UniTope on seen and unseen alleles to the performance of ANN. Subsequently, other pan-specific prediction methods will be considered. We employ the IEDB benchmark derived IEDBh9 data set, which was introduced in Section 4.2. The IC50 values are log-transformed into the range [0, 1] according to [75]: 1 − log(IC50) log(50,000) , yielding high scores for low IC50 values, i.e., high binding affinities, and low scores for high IC50 values, i.e., low binding affinities. As performance measure the Pearson correlation coefficient (PCC, see equation 3.27) is employed.
4.3. MHC BINDING PREDICTION FOR ALL MHC-I ALLELES 41 Figure 4.6: Correlation between UniTope performance and distance to nearest neighbor. The PCC achieved by UniTope on unseen alleles on a specific MHC allele is plotted against the allele’s distance to its nearest neighbor. The PCC between UniTope performance and distance to nearest neighbor is −0.59. The regression line is plotted as solid red line. One and two standard deviations are plotted as gray dashed and dashed-dotted line, respectively. MHC alleles discussed in the text are shown in blue. The average PCC yielded by ANN on the IEDB h9 data set is 0.54 with a minimum of 0.19 (HLA-B*40:01) and a maximum of 0.83 (HLA-B*18:01). The individual performances are listed in Table B.5 in the appendix. Performance on Unseen Alleles Performance on unseen alleles is measured via a leave-one-out validation. In leave-one-out validation the data for one allele, the unseen allele, is omitted from training. A model selection is performed on the remaining data and the final model is used for predictions on the unseen allele. In this setting, the performance of UniTope on unknown MHC specificities is simulated. Leave-one-out validation is performed for each allele represented in the IEDB h9 data set. We determine the best parameter combination via five-fold cross validation on the training set. On average, UniTope yields a PCC of 0.55 with a minimum of 0.01 (HLA-B*27:05) and a maximum of 0.82 (HLA-A*02:06). While this definitely leaves room for improvement, it is on par with the performance of the allele-specific ANN. The individual performances of UniTope are listed in Table B.6 in the appendix. UniTope performs particularly well on HLA-A*02:06 and produces merely random predictions for HLA-B*27:05. What determines whether UniTope will perform well on an allele? In order to analyze this, we consider each allele’s distance to its nearest neighbor in the IEDB h9 data set. The distance between two alleles a and b is defined as the Euclidean
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 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 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 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
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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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