Immunological Bioinformatics

by Lund, Ole; Nielsen, Morten; Lundegaard, Claus; Kesmir, Can; Brunak, Soren

• ISBN13: 9780262122801
• ISBN10: 0262122804
• Format: Hardcover
• Copyright: 2005-06-17
• Publisher: The MIT Press

Summary

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.

Author Biography

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 and Claus Lundegaard are Associate Professors, and Soren Brunak is Professor and Center Director Can Kesmir is Assistant Professor at the Department of Theoretical Biology at Utrecht University

Table of Contents

Preface	ix
1 Immune Systems and Systems Biology	1	(16)
1.1 Innate and Adaptive Immunity in Vertebrates	10	(1)
1.2 Antigen Processing and Presentation	11	(3)
1.3 Individualized Immune Reactivity	14	(3)
2 Contemporary Challenges to the Immune System	17	(18)
2.1 Infectious Diseases in the New Millennium	17	(1)
2.2 Major Killers in the World	17	(4)
2.3 Childhood Diseases	21	(1)
2.4 Clustering of Infectious Disease Organisms	22	(2)
2.5 Biodefense Targets	24	(6)
2.6 Cancer	30	(1)
2.7 Allergy	31	(1)
2.8 Autoimmune Diseases	32	(3)
3 Sequence Analysis in Immunology	35	(34)
3.1 Sequence Analysis	35	(1)
3.2 Alignments	36	(16)
3.3 Multiple Alignments	52	(2)
3.4 DNA Alignments	54	(1)
3.5 Molecular Evolution and Phylogeny	55	(2)
3.6 Viral Evolution and Escape: Sequence Variation	57	(4)
3.7 Prediction of Functional Features of Biological Sequences	61	(8)
4 Methods Applied in Immunological Bioinformatics	69	(34)
4.1 Simple Motifs, Motifs and Matrices	69	(3)
4.2 Information Carried by Immunogenic Sequences	72	(3)
4.3 Sequence Weighting Methods	75	(2)
4.4 Pseudocount Correction Methods	77	(2)
4.5 Weight on Pseudocount Correction	79	(1)
4.6 Position Specific Weighting	79	(1)
4.7 Gibbs Sampling	80	(4)
4.8 Hidden Markov Models	84	(7)
4.9 Artificial Neural Networks	91	(8)
4.10 Performance Measures for Prediction Methods	99	(3)
4.11 Clustering and Generation of Representative Sets	102	(1)
5 DNA Microarrays in Immunology	103	(8)
5.1 DNA Microarray Analysis	103	(3)
5.2 Clustering	106	(2)
5.3 Immunological Applications	108	(3)
6 Prediction of Cytotoxic T Cell (MHC Class I) Epitopes	111	(24)
6.1 Background and Historical Overview of Methods for Pep-tide MHC Binding Prediction	112	(2)
6.2 MHC Class I Epitope Binding Prediction Trained on Small Data Sets	114	(6)
6.3 Prediction of CTL Epitopes by Neural Network Methods	120	(13)
6.4 Summary of the Prediction Approach	133	(2)
7 Antigen Processing in the MHC Class I Pathway	135	(22)
7.1 The Proteasome	135	(2)
7.2 Evolution of the Immunosubunits	137	(2)
7.3 Specificity of the (Immuno)Proteasome	139	(4)
7.4 Predicting Proteasome Specificity	143	(4)
7.5 Comparison of Proteasomal Prediction Performance	147	(2)
7.6 Escape from Proteasomal Cleavage	149	(1)
7.7 Post-Proteasomal Processing of Epitopes	150	(3)
7.8 Predicting the Specificity of TAP	153	(1)
7.9 Proteasome and TAP Evolution	154	(3)
8 Prediction of Helper T Cell (MHC Class II) Epitopes	157	(18)
8.1 Prediction Methods	158	(1)
8.2 The Gibbs Sampler Method	159	(13)
8.3 Further Improvements of the Approach	172	(3)
9 Processing of MHC Class II Epitopes	175	(12)
9.1 Enzymes Involved in Generating MHC Class II Ligands	176	(3)
9.2 Selective Loading of Peptides to MHC Class II Molecules	179	(1)
9.3 Phylogenetic Analysis of the Lysosomal Proteases	180	(2)
9.4 Signs of the Specificities of Lysosomal Proteases on MHC Class II Epitopes	182	(1)
9.5 Predicting the Specificity of Lysosomal Enzymes	182	(5)
10 B Cell Epitopes	187	(16)
10.1 Affinity Maturation	188	(3)
10.2 Recognition of Antigen by B cells	191	(10)
10.3 Neutralizing Antibodies	201	(2)
11 Vaccine Design	203	(12)
11.1 Categories of Vaccines	204	(3)
11.2 Polytope Vaccine: Optimizing Plasmid Design	207	(2)
11.3 Therapeutic Vaccines	209	(4)
11.4 Vaccine Market	213	(2)
12 Web-Based Tools for Vaccine Design	215	(8)
12.1 Databases of MHC Ligands	215	(2)
12.2 Prediction Servers	217	(6)
13 MHC Polymorphism	223	(20)
13.1 What Causes MHC Polymorphism?	223	(2)
13.2 MHC Supertypes	225	(18)
14 Predicting Immunogenicity: An Integrative Approach	243	(11)
14.1 Combination of MHC and Proteasome Predictions	244	(1)
14.2 Independent Contributions from TAP and Proteasome Predictions	245	(2)
14.3 Combinations of MHC, TAP, and Proteasome Predictions	247	(4)
14.4 Validation on HIV Data Set	251	(1)
14.5 Perspectives on Data Integration	252	(2)
References	254	(37)
Index	291