New Approaches to in silico Design of Epitope-Based Vaccines

More documents

Recommendations

Info

50 CHAPTER 4. EPITOPE DISCOVERY a direct comparison is not possible. However, the performance of the WD-kernel-based predictor should provide a reasonable estimate. It yields a mean auROC of 0.70, which is well below the performance of our combined model. 4.4.5 Discussion The potency of an EV strongly depends on the capability of the included peptides to induce an immune response. The development of prediction methods capable of accurately predicting T-cell epitopes is thus of major interest to immunologists and the pharmaceutical industry. Whether a particular peptide is a T-cell epitope or not depends on the availability of an MHC molecule that presents the peptide on the cell surface and on the availability of a suitable TCR for the specific pMHC complex. The latter is determined by the host’s proteome. These complex dependencies and the incomplete biological knowledge, render the prediction of T-cell epitopes particularly challenging. The accuracy of current prediction methods is too low to permit their use in most biomedical applications. Reasons for this can be found in the choice of training data and in the methods’ limited view on T-cell reactivity. Previously proposed methods employ peptide sequence information only. We propose to move from simple sequence-based predictors to predictors that take relevant system-wide properties like the self-tolerance of the immune system into account. Using a carefully designed set of EBV peptides provided by the group of Stefan Stevanović (Department of Immunology, University of Tübingen) we could show that the incorporation of a model of self-tolerance considerably improves T-cell epitope prediction. Our model of self-tolerance is based on the similarity of the target peptides to a set of self-peptides derived from a representative reference proteome. Three different reference proteomes were considered: thymus-min and thymus-max for modelling central tolerance and IPI for modelling central as well as peripheral tolerance. Employment of the IPIbased model for T-cell epitope prediction does not yield an improvement over sequencebased predictions. However, employment of a model of central tolerance via the nonconservatively defined thymus proteome, thymus-max, does. This indicates that our model of self-tolerance represents central tolerance more accurately than peripheral tolerance, despite the fact that we use a probably incomplete thymus proteome. Mechanisms of peripheral tolerance are responsible for preventing self-reactive T cells in the periphery from inducing an immune response. Such T cells have escaped negative selection in the thymus either because (a) they display a low avidity for self-antigens presented in the thymus [100] or because (b) their cognate self-antigen is not expressed in the thymus [32]. The first case applies to proteins from the thymus proteome. This aspect of peripheral tolerance could thus be covered by our model of central tolerance: peptides similar to self-peptides expressed in the thymus are unlikely to be immunogenic. The second case applies to tissuespecific proteins not presented in the thymus. Naive T cells circulate between blood and secondary lymphoid organs. Thus, the encounter of a naive T cell with an APC presenting peptides derived from tissue-specific proteins is highly unlikely [101]. Consequently, naive T cells reactive to tissue-specific proteins are not necessarily deactivated by peripheral
4.4. T-CELL EPITOPE PREDICTION 51 tolerance mechanisms. The assumption that peptides similar to self-peptides in general are not likely to be immunogenic does not apply, rendering our model of self-tolerance inappropriate to represent peripheral tolerance. It has to be noted that this study is based on a single small data set comprising immunogenicity data with respect to one virus and one MHC allele. Performing multiple runs of two-times nested five-fold cross validation ensures the generation of statistically significant results. However, more – and preferably larger – data sets for other alleles are needed to evaluate the general validity of our approach, including the model of self-tolerance, the feature encoding and the choice of distance measure. The amount of immunogenicity data available from the IEDB [87] is continuously increasing. However, due to the variance caused by the use of various experimental approaches in various laboratories, the vast majority of this data does not fulfill the strict requirements we impose. Thus, as of now, a thorough, statistically correct study on the small but consistent EBV data set is the best we can do. To the best of our knowledge this is the first study predicting T-cell epitopes incorporating system-wide properties. Despite the small sample size, the work presented here demonstrates a proof-of-concept for the incorporation of a model of self-tolerance for T-cell epitope prediction.
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
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: 4.4. T-CELL EPITOPE PREDICTION 49 F
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
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.
Page 133 and 134:
BIBLIOGRAPHY 121 [97] H. Parkinson,
Page 135 and 136:
BIBLIOGRAPHY 123 [123] NCBI dbMHC d
Page 137 and 138:
BIBLIOGRAPHY 125 [150] World Health
show all

New Approaches to in silico Design of Epitope-Based Vaccines

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?