New Approaches to in silico Design of Epitope-Based Vaccines

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76 CHAPTER 7. APPLICATIONS The advanced options offer the possibility to set an immunogenicity threshold, i.e., a minimum immunogenicity score required for a peptide to be considered immunogenic with respect to a specific allele. Only peptides which score above this threshold for at least one MHC allele will be considered during epitope selection. Step 4: Results The results page gives a summary of the input data and the selected constraints as well as the results of the optimization. If the optimization problem is feasible, a table containing the optimal set of epitopes will be displayed (Figure 7.2). For every epitope in the set the following information is given: its fraction of the overall immunogenicity, a list of the MHC alleles it covers and, if antigen information was given, the corresponding antigens. The user can switch to a more detailed results table, which contains additional information on epitope conservation and immunogenicities. Information on the size of the selected set, the number of covered alleles and on the number of covered antigens, if applicable, is displayed above the table. Furthermore, the coverage of each of the given MHC loci and the corresponding population coverage are given. (If locus A has a coverage of 75%, the probability of an individual from the target population carrying a covered allele at locus A is 75%. A population coverage of 80% corresponds to a probability of 80% for an individual from the target population to carry at least one of the covered alleles.) The results can be downloaded. A choice of two file formats is given: XLS (MS Excel) and CSV (comma separated values). For typical problem sizes, OptiTope finds an optimal set of peptides within seconds. Nevertheless, the user can choose to be notified of the completion of the request via e-mail. If the optimization problem is infeasible, meaning that no set of epitopes from the given antigenic sequences fulfills all requirements, a basic analysis of the problem is performed. Based on this analysis, OptiTope suggests constraint modifications that might result in a feasible problem. If the basic analysis does not yield a possible explanation for the infeasibility, OptiTope will suggest to deselect individual constraints or to increase the number of epitopes to be selected. 7.1.2 Implementation OptiTope is incorporated into the website EpiToolKit [124], which is based on the Zope application server [125], and the content management system Plone [126]. For the user interface, we employ dynamic HTML with CSS and JavaScript. Python scripts are used for data validation and processing. OptiTope was thoroughly tested for compatibility with the popular web browsers Mozilla Firefox (version 3.0.5) and Microsoft Internet Explorer (version 7). OptiTope uses the GNU Linear Programming Kit GLPK [55] and the GNU MathProg modeling language GMPL to formulate and solve the optimization problems. 7.1.3 Discussion With OptiTope, we provide an easy-to-use tool that assists immunologists in designing EVs. Given a set of antigenic sequences of interest, a target population and special requirements
7.2. DESIGN OF A PEPTIDE COCKTAIL VACCINE 77 Figure 7.2: A screenshot from the results page of OptiTope. of the user, OptiTope efficiently determines an optimal set of epitopes. To our knowledge, OptiTope is the first web-based approach for optimal vaccine design. Currently, OptiTope only offers immunogenicity predictions for MHC-I, i.e., the only way to include MHC-II in the selection process is via a table of epitopes and their immunogenicities with respect to specific MHC alleles. An inclusion of MHC-II prediction methods along with further MHC-I prediction methods would be desirable. Furthermore, the results page could be enhanced by linking selected epitopes that can be found in the IEDB [87] to the corresponding IEDB site. 7.2 Design of a Peptide Cocktail Vaccine The focus of the first vaccine design study is on evaluating the performance of the epitope selection framework presented in Chapter 5 in comparison to the heuristic approach proposed by Vider-Shalit et al. [24]. Vider-Shalit et al. use a genetic algorithm to design a string-of-beads EV against HCV, comprising 25 epitopes. Employing our epitope selection framework and two different definitions of good vaccine, we design HCV peptide cocktail vaccines. The resulting vaccines outperform the peptide cocktail corresponding to the EV proposed in [24] with respect to various quality criteria including overall immunogenicity and population coverage. 7.2.1 Materials & Methods Protein sequences. HCV protein sequences (AA frame 1) for ten different proteins (C, E1, E2, p7, NS2, NS3, NS4A, NS4B, NS5A, NS5B) and four different subtypes (1a, 1b, 2a, 3a) were retrieved from the Los Alamos hepatitis C sequence database [127]. (Unfortunately, Vider-Shalit et al. could not provide us with the protein sequences they used.) For each protein of each subtype an MSA was created using MUSCLE [128], resulting in 40 MSAs. From each MSA a consensus sequence was created. All ninemers from these consensus sequences were regarded as potential epitopes.
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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
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3.1. COMBINATORIAL OPTIMIZATION 19
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3.2. MACHINE LEARNING 21 Figure 3.2
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3.2. MACHINE LEARNING 23 Figure 3.3
Page 37 and 38: 3.2. MACHINE LEARNING 25 Figure 3.4
Page 39 and 40: 3.2. MACHINE LEARNING 27 Figure 3.5
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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
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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
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New Approaches to in silico Design of Epitope-Based Vaccines

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