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Bioinformatics for Vaccinology

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“… this book was written from start to finish by one extremely dedicated and erudite individual. The author has done an excellent job of covering the many topics that fall under the umbrella of computational biology for vaccine design, demonstrating an admirable command of subject matter in fields as disparate as object-oriented databases and regulation of T cell response. Simply put, it has just the right breadth and depth, and it reads well. In fact, readability is one of its virtues—making the book enticing and useful, all at once…” Human Vaccines, 2010

''... This book has several strong points. Although there are many textbooks that deal with vaccinology, few attempts have been made to bring together descriptions of vaccines in history, basic bioinformatics, various computational solutions and challenges in vaccinology, detailed experimental methodologies, and cutting-edge technologies... This book may well serve as a first line of reference for all biologists and computer scientists...'' –Virology Journal, 2009

Vaccines have probably saved more lives and reduced suffering in a greater number of people than any other medical intervention in human history, succeeding in eradicating smallpox and significantly reducing the mortality and incidence of other diseases. However, with the emergence of diseases such as SARS and the threat of biological warfare, vaccination has once again become a topic of major interest in public health.В 

Vaccinology now has at its disposal an array of post-genomic approaches of great power. None has a more persuasive potential impact than the application of computational informatics to vaccine discovery; the recent expansion in genome data and the parallel increase in cheap computing power have placed the bioinformatics exploration of pathogen genomes centre stage for vaccine researchers.В 

This is the first book to address the area of bioinformatics as applied to rational vaccine design, discussing the ways in which bioinformatics can contribute to improved vaccine development by

  • introducing the subject of harnessing the mathematical and computing power inherent in bioinformatics to the study of vaccinology
  • putting it into a historical and societal context, andВ 
  • exploring the scope of its methods and applications.

Bioinformatics for Vaccinology is a one-stop introduction to computational vaccinology. It will be of particular interest to bioinformaticians with an interest in immunology, as well as to immunologists, and other biologists who need to understand how advances in theoretical and computational immunobiology can transform their working practices.

Author(s): Darren R. Flower
Edition: 1
Publisher: Wiley
Year: 2008

Language: English
Commentary: 70272
Pages: 302

Bioinformatics for Vaccinology......Page 3
Contents......Page 9
Preface......Page 15
Acknowledgements......Page 17
Exordium......Page 19
Smallpox in history......Page 23
Variolation......Page 25
Variolation in history......Page 27
Variolation comes to Britain......Page 28
Lady Mary Wortley Montagu......Page 31
Variolation and the Sublime Porte......Page 33
The royal experiment......Page 35
The boston connection......Page 36
Variolation takes hold......Page 39
The Suttonian method......Page 40
Variolation in Europe......Page 41
The coming of vaccination......Page 43
Edward Jenner......Page 45
Cowpox......Page 48
Vaccination vindicated......Page 50
Louis Pasteur......Page 51
Vaccination becomes a science......Page 52
Meister, Pasteur and rabies......Page 53
A vaccine for every disease......Page 55
In the time of cholera......Page 56
Haffkine and cholera......Page 58
Bubonic plague......Page 59
The changing face of disease......Page 61
Almroth wright and typhoid......Page 62
Tuberculosis, Koch, and Calmette......Page 65
Vaccine BCG......Page 66
Poliomyelitis......Page 68
Salk and Sabin......Page 69
Diphtheria......Page 71
Whooping cough......Page 72
Many diseases, many vaccines......Page 73
Smallpox: Endgame......Page 75
Further reading......Page 76
Eradication and reservoirs......Page 77
Lifespans......Page 79
The evolving nature of disease......Page 81
Three threats......Page 82
Tuberculosis in the 21 st century......Page 83
HIV and AIDS......Page 84
Malaria: Then and now......Page 85
Influenza......Page 86
Bioterrorism......Page 87
Vaccines as medicines......Page 89
Vaccines and the pharmaceutical industry......Page 90
The coming of the vaccine industry......Page 92
Challenging the immune system......Page 95
The threat from bacteria: Robust, diverse, and endemic......Page 96
Microbes, diversity and metagenomics......Page 97
The intrinsic complexity of the bacterial threat......Page 98
Microbes and humankind......Page 99
The nature of vaccines......Page 100
Types of vaccine......Page 102
Epitopic vaccines......Page 104
Vaccine delivery......Page 105
Emerging immunovaccinology......Page 106
The immune system......Page 107
Innate immunity......Page 108
Adaptive immunity......Page 110
Cellular components of immunity......Page 112
The T cell repertoire......Page 115
Epitopes: The immunological quantum......Page 116
The major histocompatibility complex......Page 117
MHC nomenclature......Page 119
Peptide binding by the MHC......Page 120
The structure of the MHC......Page 121
The proteasome......Page 123
Class II processing......Page 125
Seek simplicity and then distrust it......Page 126
Cross presentation......Page 127
T cell receptor......Page 128
T cell activation......Page 130
Signal 1, signal 2, immunodominance......Page 131
Humoral immunity......Page 132
Further reading......Page 134
Making sense of data......Page 135
Knowledge in a box......Page 136
The proteome......Page 137
Systems biology......Page 138
The immunome......Page 139
Databases and databanks......Page 140
The XML database......Page 141
The protein universe......Page 142
What proteins do......Page 144
The amino acid world......Page 146
The chiral nature of amino acids......Page 149
Naming the amino acids......Page 152
The amino acid alphabet......Page 154
Defining amino acid properties......Page 156
Size, charge and hydrogen bonding......Page 157
Hydrophobicity, lipophilicity and partitioning......Page 158
Understanding partitioning......Page 161
Charges, ionization, and pka......Page 162
Many kinds of property......Page 165
Mapping the world of sequences......Page 168
Biological sequence databases......Page 169
Nucleic acid sequence databases......Page 170
Protein sequence databases......Page 171
Annotating databases......Page 172
Text mining......Page 173
Ontologies......Page 175
Secondary sequence databases......Page 176
Other databases......Page 177
Host databases......Page 178
Pathogen databases......Page 181
Functional immunological databases......Page 183
Composite, integrated databases......Page 184
Allergen databases......Page 185
Reference......Page 187
Towards epitope-based vaccines......Page 189
T cell epitope prediction......Page 190
Predicting MHC binding......Page 191
Binding is biology......Page 194
Quantifying binding......Page 195
Entropy, enthalpy and entropy-enthalpy compensation......Page 196
Experimental measurement of binding......Page 197
Modern measurement methods......Page 199
Isothermal titration calorimetry......Page 200
Long and short of peptide binding......Page 201
The class I peptide repertoire......Page 202
Practicalities of binding prediction......Page 203
Binding becomes recognition......Page 204
Immunoinformatics lends a hand......Page 205
Motif based prediction......Page 206
The imperfect motif......Page 207
Other approaches to binding prediction......Page 208
Representing sequences......Page 209
Artificial neural networks......Page 210
Support vector machines......Page 212
Partial least squares......Page 213
Quantitative structure activity relationships......Page 214
Other techniques and sequence representations......Page 215
Amino acid properties......Page 216
Direct epitope prediction......Page 217
Predicting antigen presentation......Page 218
Predicting class II MHC binding......Page 219
Assessing prediction accuracy......Page 221
ROC plots......Page 224
Quantitative accuracy......Page 225
Prediction assessment protocols......Page 226
Comparing predictions......Page 228
Prediction versus experiment......Page 229
Predicting B cell epitopes......Page 230
Peak profiles and smoothing......Page 231
Early methods......Page 232
Imperfect B cell prediction......Page 233
References......Page 234
Structure and function......Page 239
Types of protein structure......Page 241
Protein folding......Page 242
Ramachandran plots......Page 243
Local structures......Page 244
Comparing structures......Page 245
Experimental structure determination......Page 246
Structural genomics......Page 248
Protein structure databases......Page 249
Other databases......Page 250
Immunological structural databases......Page 251
Small molecule databases......Page 252
Protein homology modelling......Page 253
Using homology modelling......Page 254
Predicting MHC supertypes......Page 255
Application to alloreactivity......Page 257
3D-QSAR......Page 258
Predicting B cell epitopes with docking......Page 260
Virtual screening......Page 262
Limitations to virtual screening......Page 263
Predicting epitopes with virtual screening......Page 265
Virtual screening and adjuvant discovery......Page 266
Adjuvants and innate immunity......Page 267
Small molecule adjuvants......Page 268
Molecular dynamics and immunology......Page 270
Molecular dynamics and binding......Page 271
Immunological applications......Page 272
Limitations of molecular dynamics......Page 273
Molecular dynamics and high performance computing......Page 274
References......Page 275
Vaccines and the world......Page 279
Bioinformatics and the challenge for vaccinology......Page 281
Predicting immunogenicity......Page 282
Computational vaccinology......Page 283
Beyond empirical vaccinology......Page 284
Designing new vaccines......Page 285
The perfect vaccine......Page 286
Conventional approaches......Page 287
Size of a genome......Page 288
Reverse vaccinology......Page 290
Finding antigens......Page 291
The success of reverse vaccinology......Page 293
Tumour vaccines......Page 295
Prediction and personalised medicine......Page 296
Imperfect data......Page 298
Forecasting and the future of computational vaccinology......Page 299
Index......Page 305