New Approaches to in silico Design of Epitope-Based Vaccines

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70 CHAPTER 6. EPITOPE ASSEMBLY A B C Figure 6.4: Cleavage patterns of the designed string-of-beads constructs. The fraction of C-termini of the vaccine epitopes predicted to be cleaved (A) and the mean cleavage score of the C-termini of the vaccine epitopes (B) are shown as red diamonds for the optimized and as blue circles with error bars for the randomly generated string-of-beads constructs. C) Mean fractions (Panel A) and mean cleavage scores (Panel B) of the C-termini of vaccine epitopes predicted to be cleaved over all different-sized string-of-beads constructs. biased towards cleavage after AAs common in C-terminal positions of MHC-I binders. However, the difference between cleavage scores for vaccine and junctional epitopes is more pronounced in the optimized string-of-beads constructs compared to the random constructs. A combination of the IBM ILOG CPLEX [53], the GNU Linear Programming Kit GLPK [55] and the GNU MathProg modeling language GMPL was used to formulate and solve the epitope ordering ILPs. (For more details on the implementation refer to Section 7.3.) 6.5 Discussion A crucial step in the construction of a string-of-beads vaccine is the epitope assembly. An unfavorable epitope order can result in the degradation of the vaccine epitopes and in the generation of unintended junctional epitopes. Since manual determination of an optimal epitope order is infeasible, we propose a graph-based approach that optimally assembles a set of epitopes into a string-of-beads construct. We could show that our algorithm finds favorable epitope orderings efficiently. This problem had not been addressed computationally before. Formulating the epitope ordering problem in graph-theoretic terms and relating it to the TSP allowed us to benefit from the wealth of research that has been done on solving the TSP. We employed two previously published approaches to solving the TSP: an exact ILP approach and the highly effective LKH. Although the heuristic is considerably faster than the ILP solver CPLEX, both find the optimal solution for realistically-sized
6.5. DISCUSSION 71 epitope sets within seconds. The freely available ILP solver GNU Linear Programming Kit GLPK [55], which easily solved the epitope selection problem described in the previous chapter, is significantly slower (130 s for 30 epitopes, more than a day for 40 epitopes) but still reasonably for typical problem sizes. Our experiments show that the optimal epitope ordering displays more favorable in silico cleavage patterns than random orderings. However, the actual extent of the advantage of optimized string-of-beads constructs over random constructs stands or falls with the accuracy of the employed proteasomal cleavage predictor. State-of-the-art proteasomal cleavage predictors yield good accuracies but leave room for improvement. Nevertheless, even based on a poor predictor the resulting cleavage pattern can be expected not to be worse than one obtained without an epitope ordering algorithm. Several experimental groups [110, 120, 121] have suggested to introduce spacer sequences between the epitopes to improve epitope recovery and prevent the cleavage of junctional epitopes. While we focused on the construction of string-of-beads constructs without spacers, our assembly approach can easily be adapted to include spacers: Given a spacer s, the edge (a, b) is weighted by the sum of the log-probabilities that a and b will be cleaved properly from the sequence a − s − b, respectively. However, depending on the length of the chosen spacer and the number of flanking residues required by the chosen proteasomal cleavage predictor, the predicted cleavage probabilities of the vaccine epitopes might depend solely on the spacer sequence. The cleaved fragment predictor proposed by Ginodi et al. [119], for example, requires only one flanking residue to determine the cleavage score of an epitope. Hence, a spacer length of one AA or more will result in the same cleavage scores for the vaccine epitopes independent of their order within the polypeptide. In this setting, it would be expedient to employ the number of unwanted epitopes expected to be generated from the sequence a − s − b or the probabilities of these unwanted epitopes to weigh the edge (a, b).
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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
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
Page 61 and 62: 4.4. T-CELL EPITOPE PREDICTION 49 F
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
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: 6.4. EXPERIMENTAL RESULTS 69 Figure
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
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 and 122: Appendix D Contributions Chapter 4
Page 123 and 124: Appendix E Publications Published M
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.
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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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