Immunological Bioinformatics

Ole Lund, Morten Nielsen, Claus Lundegaard, Can Kesmir, Søren Brunak

  • 出版商: The MIT Press
  • 出版日期: 2005-06-17
  • 定價: $1,700
  • 售價: 6.0$1,020
  • 語言: 英文
  • 頁數: 312
  • 裝訂: Hardcover
  • ISBN: 0262122804
  • ISBN-13: 9780262122801
  • 相關分類: Bioinformatics 生物資訊

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Description:

Despite the fact that advanced bioinformatics methodologies have not been used as extensively in immunology as in other subdisciplines within biology, research in immunological bioinformatics has already developed models of components of the immune system that can be combined and that may help develop therapies, vaccines, and diagnostic tools for such diseases as AIDS, malaria, and cancer.

In a broader perspective, specialized bioinformatics methods in immunology make possible for the first time a systems-level understanding of the immune system. The traditional approaches to immunology are reductionist, avoiding complexity but providing detailed knowledge of a single event, cell, or molecular entity. Today, a variety of experimental bioinformatics techniques connected to the sequencing of the human genome provides a sound scientific basis for a comprehensive description of the complex immunological processes.

This book offers a description of bioinformatics techniques as they are applied to immunology, including a succinct account of the main biological concepts for students and researchers with backgrounds in mathematics, statistics, and computer science as well as explanations of the new data-driven algorithms in the context of biological data that will be useful for immunologists, biologists, and biochemists working on vaccine design. In each chapter the authors show interesting biological insights gained from the bioinformatics approach. The book concludes by explaining how all the methods presented in the book can be integrated to identify immunogenic regions in microorganisms and host genomes.

Ole Lund is Associate Professor and leader of the Immunological Bioinformatics group at the Center for Biological Sequence Analysis at Technical University of Denmark.

Morten Nielsen is Associate Professor at the Center for Biological Sequence Analysis at Technical University of Denmark.

Claus Lundegaard is Associate Professor at the Center for Biological Sequence Analysis at Technical University of Denmark.

Can Kesmir is Assistant Professor in the Department of Theoretical Biology at Utrecht University.

Søren Brunak is Professor and Director of the Center for Biological Sequence Analysis at the Technical University of Denmark.


 

Table of Contents:

Preface ix
1 Immune Systems and Systems Biology 1
1.1 Innate and Adaptive Immunity in Vertebrates 10
1.2 Antigen Processing and Presentation 11
1.3 Individualized Immune Reactivity 14
2 Contemporary Challenges to the Immune System 17
2.1 Infectious Diseases in the New Millennium 17
2.2 Major Killers in the World 17
2.3 Childhood Diseases 21
2.4 Clustering of Infectious Disease Organisms 22
2.5 Biodefense Targets 24
2.6 Cancer 30
2.7 Allergy 31
2.8 Autoimmune Diseases 32
3 Sequence Analysis in Immunology 35
3.1 Sequence Analysis 35
3.2 Alignments 36
3.3 Multiple Alignments 52
3.4 DNA Alignments 54
3.5 Molecular Evolution and Phylogeny 55
3.6 Viral Evolution and Escape: Sequence Variation 57
3.7 Prediction of Functional Features of Biological Sequences 61
4 Methods Applied in Immunological Bioinformatics 69
4.1 Simple Motifs, Motifs and Matrices 69
4.2 Information Carried by Immunogenic Sequences 72
4.3 Sequence Weighting Methods 75
4.4 Pseudocount Correction Methods 77
4.5 Weight on Pseudocount Correction 79
4.6 Position Specific Weighting 79
4.7 Gibbs Sampling 80
4.8 Hidden Markov Models 84
4.9 Artificial Neural Networks 91
4.10 Performance Measures for Prediction Methods 99
4.11 Clustering and Generation of Representative Sets 102
5 DNA Microarrays in Immunology 103
5.1 DNA Microarray Analysis 103
5.2 Clustering 106
5.3 Immunological Applications 108
6 Prediction of Cytotoxic T Cell (MHC Class I) Epitopes 111
6.1 Background and Historical Overview of Methods for Peptide MHC Binding Prediction 112
6.2 MHC Class I Epitope Binding Prediction Trained on Small Data Sets 114
6.3 Prediction of CTL Epitopes by Neural Network Methods 120
6.4 Summary of the Prediction Approach 133
7 Antigen Processing in the MHC Class I Pathway 135
7.1 The Proteasome 135
7.2 Evolution of the Immunosubunits 137
7.3 Specificity of the (Immuno)Proteasome 139
7.4 Predicting Proteasome Specificity 143
7.5 Comparison of Proteasomal Prediction Performance 147
7.6 Escape from Proteasomal Cleavage 149
7.7 Post-Proteasomal Processing of Epitopes 150
7.8 Predicting the Specificity of TAP 153
7.9 Proteasome and TAP Evolution 154
8 Prediction of Helper T Cell (MHC Class II) Epitopes 157
8.1 Prediction Methods 158
8.2 The Gibbs Sampler Method 159
8.3 Further Improvements of the Approach 172
9 Processing of MHC Class II Epitopes 175
9.1 Enzymes Involved in Generating MHC Class II Ligands 176
9.2 Selective Loading of Peptides to MHC Class II Molecules 179
9.3 Phylogenetic Analysis of the Lysosomal Proteases 180
9.4 Signs of the Specificities of Lysosomal Proteases on MHC Class II Epitopes 182
9.5 Predicting the Specificity of Lysosomal Enzymes 182
10 B Cell Epitopes 187
10.1 Affinty Maturation 188
10.2 Recognition of Antigen by B Cells 191
10.3 Neutralizing Antibodies 201
11 Vaccine Design 203
11.1 Categories of Vaccines 204
11.2 Polytope Vaccine: Optimizing Plasmid Design 207
11.3 Therapeutic Vaccines 209
11.4 Vaccine Market 213
12 Web-Based Tools for Vaccine Design 215
12.1 Databases of MHC Ligands 215
12.2 Prediction Servers 217
13 MHC Polymorphism 223
13.1 What Causes MHC Polymorphism? 223
13.2 MHC Supertypes 225
14 Predicting Immunogenicity: An Integrative Approach 243
14.1 Combination of MHC and Proteasome Predictions 244
14.2 Independent Contributions from TAP and Proteasome Predictions 245
14.3 Combinations of MHC, TAP, and Proteasome Predictions 247
14.4 Validation on HIV Data Set 251
14.5 Perspectives on Data Integration 252
References 254
Index 291