Immunological Bioinformatics
內容描述
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
