New Approaches to in silico Design of Epitope-Based Vaccines

More documents

Recommendations

Info

116 BIBLIOGRAPHY [32] K. A. Hogquist, T. A. Baldwin, and S. C. Jameson. Central tolerance: learning self-control in the thymus. Nat Rev Immunol, 5(10):772–82, 2005. [33] K. Wing and S. Sakaguchi. Regulatory T cells exert checks and balances on self tolerance and autoimmunity. Nat Immunol, 11(1):7–13, 2010. [34] H. G. Rammensee, T. Friede, and S. Stevanović. MHC ligands and peptide motifs: first listing. Immunogenetics, 41(4):178–228, 1995. [35] R. A. Seder, P. A. Darrah, and M. Roederer. T-cell quality in memory and protection: implications for vaccine design. Nat Rev Immunol, 8(4):247–258, 2008. [36] C. Scheibenbogen, K. H. Lee, S. Mayer, S. Stevanović, U. Moebius, W. Herr, H.-G. Rammensee, and U. Keilholz. A sensitive ELISPOT assay for detection of CD8+ T lymphocytes specific for HLA class I-binding peptide epitopes derived from influenza proteins in the blood of healthy donors and melanoma patients. Clin Cancer Res, 3(2):221–6, 1997. [37] J. D. Altman, P. A. Moss, P. J. Goulder, D. H. Barouch, M. G. McHeyzer-Williams, J. I. Bell, A. J. McMichael, and M. M. Davis. Phenotypic analysis of antigen-specific T lymphocytes. Science, 274(5284):94–6, 1996. [38] S. Depil, O. Moralès, F. A. Castelli, N. Delhem, V. François, B. Georges, F. Dufossé, F. Morschhauser, J. Hammer, B. Maillère, C. Auriault, and V. Pancré. Determination of a HLA II promiscuous peptide cocktail as potential vaccine against EBV latency II malignancies. J Immunother, 30(2):215–226, 2007. [39] N. Yajima, R. Yamanaka, T. Mine, N. Tsuchiya, J. Homma, M. Sano, T. Kuramoto, Y. Obata, N. Komatsu, Y. Arima, A. Yamada, M. Shigemori, K. Itoh, and R. Tanaka. Immunologic evaluation of personalized peptide vaccination for patients with advanced malignant glioma. Clin Cancer Res, 11(16):5900–5911, 2005. [40] C. L. Slingluff, G. R. Petroni, G. V. Yamshchikov, D. L. Barnd, S. Eastham, H. Galavotti, J. W. Patterson, D. H. Deacon, S. Hibbitts, D. Teates, P. Y. Neese, W. W. Grosh, K. A. Chianese-Bullock, E. M. H. Woodson, C. J. Wiernasz, P. Merrill, J. Gibson, M. Ross, and V. H Engelhard. Clinical and immunologic results of a randomized phase II trial of vaccination using four melanoma peptides either administered in granulocyte-macrophage colony-stimulating factor in adjuvant or pulsed on dendritic cells. J Clin Oncol, 21(21):4016–4026, 2003. [41] A. W. Purcell, J. McCluskey, and J. Rossjohn. More than one reason to rethink the use of peptides in vaccine design. Nat Rev Drug Discov, 6(5):404–414, 2007. [42] E. Celis. Getting peptide vaccines to work: just a matter of quality control? J Clin Invest, 110(12):1765–8, 2002. [43] K. Deres, H. Schild, K. H. Wiesmüller, G. Jung, and H.-G. Rammensee. In vivo priming of virus-specific cytotoxic T lymphocytes with synthetic lipopeptide vaccine. Nature, 342(6249):561–4, 1989.
BIBLIOGRAPHY 117 [44] F. Morein and K. L. Bengtsson. Functional aspects of ISCOMs. Immunol Cell Biol, 76(4):295–9, 1998. [45] G. L. Nemhauser and L. A. Wolsey. Integer and Combinatorial Optimization. Discrete Mathematics and Optimization. Wiley-VCH, 1st edition, 1999. [46] B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2001. [47] A. Ben-Hur, C. S. Ong, S. Sonnenburg, B. Schölkopf, and G. Rätsch. Support vector machines and kernels for computational biology. PLoS Comput Biol, 4(10):e1000173, 2008. [48] A. Schrijver. Combinatorial Optimization. Algorithms and Combinatorics. Springer, 2003. [49] K. Menger. Eine neue Definition der Bogenlänge. In K. Menger, editor, Ergebnisse eines Mathematischen Kolloquiums, volume 2, pages 11–12. Teubner, Leipzig, 1932. [50] A. H. Land and A. G. Doig. An automatic method of solving discrete programming problems. Econometrica, 28(3):497–520, 1960. [51] R. E. Gomory. Outline of an algorithm for integer solutions to linear programs. Bull Amer Math Soc, 64(5):275–278, 1958. [52] M. Padberg and G. Rinaldi. A branch-and-cut algorithm for the resolution of largescale symmetric traveling salesman problems. SIAM Review, 33(1):60–100, 1991. [53] International Business Machines Corp. IBM ILOG CPLEX, version 11.1, http://www.ilog.com. [54] MOSEK ApS. Mosek, http://www.mosek.com. [55] GNU Linear Programming Kit GLPK, version 4.32, http://www.gnu.org/software/glpk. [56] S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004. [57] V. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, 1995. [58] G. Schweikert, A. Zien, G. Zeller, J. Behr, C. Dieterich, C. S. Ong, P. Philips, F. De Bona, L. Hartmann, A. Bohlen, N. Krüger, S. Sonnenburg, and G. Rätsch. mGene: accurate SVM-based gene finding with an application to nematode genomes. Genome Res, 19(11):2133–43, 2009. [59] A. Höglund, P. Dönnes, T. Blum, H. W. Adolph, and O. Kohlbacher. MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition. Bioinformatics, 22(10):1158–65, 2006.
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 35 and 36:
Page 37 and 38:
Page 39 and 40:
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 47 and 48:
Page 49 and 50:
Page 51 and 52:
4.3. MHC BINDING PREDICTION FOR ALL
Page 53 and 54:
Page 55 and 56:
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 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: BIBLIOGRAPHY 115 [19] T. Sturniolo,
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

Create successful ePaper yourself

Delete template?

Save as template?