Bioinformatics for Immunomics (eBook)

Darren D.R. Flower, Matthew Davies, Shoba Ranganathan (Herausgeber)

eBook Download: PDF

2009 | 2010
XVI, 192 Seiten
Springer New York (Verlag)
978-1-4419-0540-6 (ISBN)

Infos

Like many words, the term 'immunomics' equates to different ideas contingent on context. For a brief span, immunomics meant the study of the Immunome, of which there were, in turn, several different definitions. A now largely defunct meaning rendered the Immunome as the set of antigenic peptides or immunogenic proteins within a single microorganism - be that virus, bacteria, fungus, or parasite - or microbial population, or antigenic or allergenic proteins and peptides derived from the environment as a whole, containing also proteins from eukaryotic sources. However, times have changed and the meaning of immunomics has also changed. Other newer definitions of the Immunome have come to focus on the plethora of immunological receptors and accessory molecules that comprise the host immune arsenal. Today, Immunomics or immunogenomics is now most often used as a synonym for high-throughput genome-based immunology. This is the study of aspects of the immune system using high-throughput techniques within a conc- tual landscape borne of both clinical and biophysical thinking.

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Like many words, the term "e;immunomics"e; equates to different ideas contingent on context. For a brief span, immunomics meant the study of the Immunome, of which there were, in turn, several different definitions. A now largely defunct meaning rendered the Immunome as the set of antigenic peptides or immunogenic proteins within a single microorganism - be that virus, bacteria, fungus, or parasite - or microbial population, or antigenic or allergenic proteins and peptides derived from the environment as a whole, containing also proteins from eukaryotic sources. However, times have changed and the meaning of immunomics has also changed. Other newer definitions of the Immunome have come to focus on the plethora of immunological receptors and accessory molecules that comprise the host immune arsenal. Today, Immunomics or immunogenomics is now most often used as a synonym for high-throughput genome-based immunology. This is the study of aspects of the immune system using high-throughput techniques within a conc- tual landscape borne of both clinical and biophysical thinking.

Inhalt

Contents 6
Contributors 8
Introduction 11
Computational Vaccinology 17
Introduction 17
Epitope Prediction 20
T Cell Epitope Prediction 20
B Cell Epitope Prediction 22
Computational Identification of Virulence Factors 24
Identifying Antigens .In Silico. Using Subcellular Location Prediction 25
The Many Successes of Reverse Vaccinology 26
Developing Vectors for Vaccines Delivery 28
Discovery of Adjuvants and Immunomodulators 29
Discussion 30
References 33
The Immuno Polymorphism Database 37
Introduction 37
IPD Projects 38
IPD-MHC 38
IPD-KIR 40
Sequence Alignments in IPD-MHC and IPD-KIR 42
IPD-HPA 43
IPD-ESTDAB 46
Discussion 46
Appendix: Access and Contact 47
References 47
The IMGT/HLA Database 49
Introduction 49
Background 49
A Historical Perspective 49
The Database Today 50
Accessing the Database 52
IMGT/HLA Tools 53
Allele Search Tools 53
Searching for Similar Sequences 55
Viewing Alignments of HLA Sequences 56
Submitting New Sequences 58
Recent Developments 58
HLA Sequences in the Generalist Databanks 58
Conclusions 59
Appendix .:. Access and Contact 59
References 60
Ontology Development for the Immune Epitope Database 62
Background: Immune Epitopes and the IEDB 63
Background: Why Ontology Development? 64
Standing on the shoulders of BFO, OBO, and OBI 66
Ontology Development for the IEDB 67
Objects, Roles, and Processes 67
Relationships 68
Summary and Conclusions 70
References 71
TEPIDAS: A DAS Server for Integrating T-Cell Epitope Annotations 72
Introduction 72
The Distributed Annotation System 73
Introduction 73
The Protocol 73
The Architecture of the System 73
The DAS Registry 74
TEPIDAS 75
Annotations Served by TEPIDAS 75
TEPIDAS Query Capabilities 75
Example: Access TEPIDAS from the SPICE Graphical Client 77
Conclusion 80
References 80
Databases and Web-Based Tools for Innate Immunity 81
Introduction 81
Databases for Innate Immune System 82
The Innate Immune Database 82
Innate Immunity Interactions Database 83
Pattern Recognition Receptor Database 84
Tools for Innate Immunity 86
CTKPred 86
CytoPred 87
AntiBP 87
Conclusion 89
References 89
Structural Immunoinformatics: Understanding MHC-Peptide-TR Binding 91
Introduction 91
MPID-T and Structurally Derived Interaction Parameters 92
Interface Area Between Peptide and MHC 93
Intermolecular Hydrogen Bonds 93
Gap Volume 94
Gap Index 94
Supertype Classification Based on Structural Characteristics 94
The MHC–Peptide Docking Protocol 95
Step 1: Rigid Docking of Nonamer Termini 96
Step 2: Loop Closure of Middle Residues 97
Step 3: Refinement of Binding Register 98
Step 4: Extension of Flanking Residues 98
Epitope Prediction 98
TR/pMHC Interaction 101
Analysis of the 1OGA Complex 103
Conclusion 106
References 106
Discovery of Conserved Epitopes Through Sequence Variability Analyses 108
Introduction 108
Materials and Methods 109
MSAs 109
PVS Description and Usage 109
Results and Conclusion 110
References 114
Tunable Detectors for Artificial Immune Systems: From Model to Algorithm 115
Introduction 115
The Adaptable Lymphocyte Hypothesis 116
Investigating TAT Behaviours 118
The TAT Equation 119
The a. Parameter 120
The Perturbation E(.t.)-.I.(.t.) 121
The Excitation E(.t.) 121
Recreating Fig. .1 122
AIS-like Data Example 122
Population Patterns 125
A Framework for Degenerate Tunable Detectors 129
Patterns of Response for Engineering 130
The Algorithm Framework 130
Parameter Settings for Population Pattern Algorithm 131
The a. Parameter 132
The q. Parameter 132
The m. Parameter 132
The d. Parameter 132
Instantiation of a Degenerate Tunable Detector AIS 133
Application and Data 133
Algorithm, Settings and Response Shape 133
k-Nearest Neighbour 135
An AIS Pattern Classifier 136
Experiments and Results 137
Conclusions 138
References 139
Defining the Elusive Molecular Self 140
Introduction 140
Notions of Immunological Self 142
Reductionist Approaches to the Immune Self 145
The Molecular Definition of Self: Innate Immunity 149
The Molecular Definition of Self: Cellular Adaptive Immunology 151
The Molecular Definition of Self: CD1 Presentation 159
The Molecular Definition of Self: Humoral Adaptive Immunity 160
The Extended Molecular Self: Human Life as Symbiosis 162
Discussion 164
References 165
A Bioinformatic Platform for a Bayesian, Multiphased, Multilevel Analysis in Immunogenomics 167
Introduction 167
Bioinformatic Challenge of the Multifactorial Diseases 168
Exploring the Domain 171
Decision Support System for Design of SNP Association Studies 172
Methods 173
Comparing to Other Solutions 175
Results 175
Known Issues and Future Goals 175
Data Analysis 176
A Bayesian Primer 176
A Bayesian Network Primer 178
Bayesian Network Properties for Representing Relevance 179
The Bayesian Multilevel Data Analysis 180
Results 182
Discussion 186
Bayesian Logic for the Fusion of Knowledge and Data 188
Factual Sources 189
The Hybrid Knowledge Base 190
Conclusion 191
References 193
Index 196

Produkt-Details

• Erscheint lt. Verlag: 3.10.2009
• Reihe/Serie: Immunomics Reviews:
• Reihe/Serie: Immunomics Reviews:
• Zusatzinfo: XVI, 192 p.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Informatik ► Weitere Themen ► Bioinformatik
• Themenwelt: Studium ► 1. Studienabschnitt (Vorklinik) ► Biochemie / Molekularbiologie
• Themenwelt: Studium ► 2. Studienabschnitt (Klinik) ► Humangenetik
• Themenwelt: Studium ► Querschnittsbereiche ► Infektiologie / Immunologie
• Themenwelt: Studium ► Querschnittsbereiche ► Prävention / Gesundheitsförderung
• Themenwelt: Naturwissenschaften ► Biologie
• Themenwelt: Technik
• Schlagworte: Annotation • Bioinformatics • Databases • Genome • Instrumentation • Proteomics
• ISBN-10: 1-4419-0540-5 / 1441905405
• ISBN-13: 978-1-4419-0540-6 / 9781441905406