Flower / Davies / Ranganathan Bioinformatics for Immunomics

1. Auflage 2009
ISBN: 978-1-4419-0540-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 3, 192 Seiten

Reihe: Immunomics Reviews:

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.

Autoren/Hrsg.

Flower, Darren D. R.

Davies, Matthew

Ranganathan, Shoba

Inhaltsverzeichnis

1;Contents;6
2;Contributors;8
3;Introduction;11
4;Computational Vaccinology;17
4.1;Introduction;17
4.2;Epitope Prediction;20
4.2.1;T Cell Epitope Prediction;20
4.2.2;B Cell Epitope Prediction;22
4.3;Computational Identification of Virulence Factors;24
4.4;Identifying Antigens .In Silico. Using Subcellular Location Prediction;25
4.5;The Many Successes of Reverse Vaccinology;26
4.6;Developing Vectors for Vaccines Delivery;28
4.7;Discovery of Adjuvants and Immunomodulators;29
4.8;Discussion;30
4.9;References;33
5;The Immuno Polymorphism Database;37
5.1;Introduction;37
5.2;IPD Projects;38
5.2.1;IPD-MHC;38
5.2.2;IPD-KIR;40
5.3;Sequence Alignments in IPD-MHC and IPD-KIR;42
5.3.1;IPD-HPA;43
5.3.2;IPD-ESTDAB;46
5.4;Discussion;46
5.5;Appendix: Access and Contact;47
5.6;References;47
6;The IMGT/HLA Database;49
6.1;Introduction;49
6.1.1;Background;49
6.1.2;A Historical Perspective;49
6.1.3;The Database Today;50
6.1.4;Accessing the Database;52
6.2;IMGT/HLA Tools;53
6.2.1;Allele Search Tools;53
6.2.2;Searching for Similar Sequences;55
6.2.3;Viewing Alignments of HLA Sequences;56
6.2.4;Submitting New Sequences;58
6.2.5;Recent Developments;58
6.2.6;HLA Sequences in the Generalist Databanks;58
6.3;Conclusions;59
6.4;Appendix .:. Access and Contact;59
6.5;References;60
7;Ontology Development for the Immune Epitope Database;62
7.1;Background: Immune Epitopes and the IEDB;63
7.2;Background: Why Ontology Development?;64
7.3;Standing on the shoulders of BFO, OBO, and OBI;66
7.4;Ontology Development for the IEDB;67
7.4.1;Objects, Roles, and Processes;67
7.4.2;Relationships;68
7.5;Summary and Conclusions;70
7.6;References;71
8;TEPIDAS: A DAS Server for Integrating T-Cell Epitope Annotations;72
8.1;Introduction;72
8.2;The Distributed Annotation System;73
8.2.1;Introduction;73
8.2.2;The Protocol;73
8.2.3;The Architecture of the System;73
8.2.4;The DAS Registry;74
8.3;TEPIDAS;75
8.3.1;Annotations Served by TEPIDAS;75
8.3.2;TEPIDAS Query Capabilities;75
8.4;Example: Access TEPIDAS from the SPICE Graphical Client;77
8.5;Conclusion;80
8.6;References;80
9;Databases and Web-Based Tools for Innate Immunity;81
9.1;Introduction;81
9.2;Databases for Innate Immune System;82
9.2.1;The Innate Immune Database;82
9.2.2;Innate Immunity Interactions Database;83
9.2.3;Pattern Recognition Receptor Database;84
9.3;Tools for Innate Immunity;86
9.3.1;CTKPred;86
9.3.2;CytoPred;87
9.3.3;AntiBP;87
9.4;Conclusion;89
9.5;References;89
10;Structural Immunoinformatics: Understanding MHC-Peptide-TR Binding;91
10.1;Introduction;91
10.2;MPID-T and Structurally Derived Interaction Parameters;92
10.2.1;Interface Area Between Peptide and MHC;93
10.2.2;Intermolecular Hydrogen Bonds;93
10.2.3;Gap Volume;94
10.2.4;Gap Index;94
10.3;Supertype Classification Based on Structural Characteristics;94
10.4;The MHC–Peptide Docking Protocol;95
10.4.1;Step 1: Rigid Docking of Nonamer Termini;96
10.4.2;Step 2: Loop Closure of Middle Residues;97
10.4.3;Step 3: Refinement of Binding Register;98
10.4.4;Step 4: Extension of Flanking Residues;98
10.5;Epitope Prediction;98
10.6;TR/pMHC Interaction;101
10.7;Analysis of the 1OGA Complex;103
10.8;Conclusion;106
10.9;References;106
11;Discovery of Conserved Epitopes Through Sequence Variability Analyses;108
11.1;Introduction;108
11.2;Materials and Methods;109
11.2.1;MSAs;109
11.2.2;PVS Description and Usage;109
11.3;Results and Conclusion;110
11.4;References;114
12;Tunable Detectors for Artificial Immune Systems: From Model to Algorithm;115
12.1;Introduction;115
12.2;The Adaptable Lymphocyte Hypothesis;116
12.3;Investigating TAT Behaviours;118
12.3.1;The TAT Equation;119
12.3.1.1;The a. Parameter;120
12.3.1.2;The Perturbation E(.t.)-.I.(.t.);121
12.3.1.3;The Excitation E(.t.);121
12.3.1.4;Recreating Fig. .1;122
12.3.2;AIS-like Data Example;122
12.4;Population Patterns;125
12.5;A Framework for Degenerate Tunable Detectors;129
12.5.1;Patterns of Response for Engineering;130
12.5.2;The Algorithm Framework;130
12.5.3;Parameter Settings for Population Pattern Algorithm;131
12.5.3.1;The a. Parameter;132
12.5.3.2;The q. Parameter;132
12.5.3.3;The m. Parameter;132
12.5.3.4;The d. Parameter;132
12.6;Instantiation of a Degenerate Tunable Detector AIS;133
12.6.1;Application and Data;133
12.6.2;Algorithm, Settings and Response Shape;133
12.6.3;k-Nearest Neighbour;135
12.6.4;An AIS Pattern Classifier;136
12.6.5;Experiments and Results;137
12.7;Conclusions;138
12.8;References;139
13;Defining the Elusive Molecular Self;140
13.1;Introduction;140
13.1.1;Notions of Immunological Self;142
13.1.2;Reductionist Approaches to the Immune Self;145
13.2;The Molecular Definition of Self: Innate Immunity;149
13.3;The Molecular Definition of Self: Cellular Adaptive Immunology;151
13.3.1;The Molecular Definition of Self: CD1 Presentation;159
13.4;The Molecular Definition of Self: Humoral Adaptive Immunity;160
13.5;The Extended Molecular Self: Human Life as Symbiosis;162
13.6;Discussion;164
13.7;References;165
14;A Bioinformatic Platform for a Bayesian, Multiphased, Multilevel Analysis in Immunogenomics;167
14.1;Introduction;167
14.2;Bioinformatic Challenge of the Multifactorial Diseases;168
14.3;Exploring the Domain;171
14.4;Decision Support System for Design of SNP Association Studies;172
14.4.1;Methods;173
14.4.2;Comparing to Other Solutions;175
14.4.3;Results;175
14.4.4;Known Issues and Future Goals;175
14.5;Data Analysis;176
14.5.1;A Bayesian Primer;176
14.5.2;A Bayesian Network Primer;178
14.5.3;Bayesian Network Properties for Representing Relevance;179
14.5.4;The Bayesian Multilevel Data Analysis;180
14.5.5;Results;182
14.5.6;Discussion;186
14.6;Bayesian Logic for the Fusion of Knowledge and Data;188
14.6.1;Factual Sources;189
14.6.2;The Hybrid Knowledge Base;190
14.7;Conclusion;191
14.8;References;193
15;Index;196