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Immunoinformatics : predicting immunogenicity in silico / edited by Darren R. Flower. — Totowa, N.J. : Humana Press, c2007.—(58.17/M592/v.409) |
Contents
Contents
Preface
Contributors
Color Plates
1 Immunoinformatics and the In Silico Prediction of Immunogenicity: An Introduction
PART I: DATABASES
2 IMGT， the International ImmunoGeneTics Information System for Immunoinformatics: Methods for Querying IMGT Databases， Tools， and Web Resources in the Context of Immunoinformatics
3 The IMGTI-/HLA Database
4 IPD: The Immuno Polymorphism Database
5 SYFPEITHI: Database for Searching and T-Cell Epitope Prediction
6 Searching and Mapping of T-Cell Epitopes， MHC Binders， and TAP Binders
7 Searching and Mapping of B-Cell Epitopes in Bcipep Database
8 Searching Haptens， Carrier Proteins， and Anti-Hapten Antibodies
PART II: DEFINING HLA SUPERTYPES
9 The Classification of HLA Supertypes by GRID/CPCA and Hierarchical Clustering Methods
10 Structural Basis for HLA-A2 Supertypes
11 Definition of MHC Supertypes Through Clustering of MHC Peptide-Binding Repertoires
12 Grouping of Class I HLA Alleles Using Electrostatic Distribution Maps of the Peptide Binding Grooves
PART III: PREDICTING PEPTIDE-MI-IC BINDING
13 Prediction of Peptide-MHC Binding Using Profiles
14 Application of Machine Learning Techniques in Predicting MHC Binders
15 Artificial Intelligence Methods for Predicting T-Cell Epitopes
16 Toward the Prediction of Class I and II Mouse Maior Histocompatibility Complex-Peptide-Binding Affinity: In Silico Bioinformatic Step-by-Step Guide Using Quantitative Structure-Activity Relationships
17 Predicting the MHC-Peptide Affinity Using Some Interactive-Type Molecular Descriptors and QSAR Models
18 Implementing the Modular MHC Model for Predicting Peptide Binding
19 Support Vector Machine-Based Prediction of MHC-Binding Peptides
20 In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC
21 HLA-Peptide Binding Prediction Using Structural and Modeling Principles
22 A Practical Guide to Structure-Based Prediction of MHC-Binding Peptides
23 Static Energy Analysis of MHC Class I and Class II Peptide-Binding Affinity
24 Molecular Dynamics Simulations: Bring Biomolecular Structures Alive on a Computer
25 An lterative Approach to Class 11 Predictions
26 Building a Meta-Predictor for MHC Class II-Binding Peptides
27 Nonlinear Predictive Modeling of MHC Class II-Peptide Binding Using Bayesian Neural Networks
PART IV: PREDICTING OTHER PROPERTIES OF IMMUNE SYSTEMS
28 TAPPred Prediction of TAP-Binding Peptides in Antigens
29 Prediction Methods for B-cell Epitopes
30 HistoCheck
31 Predicting Virulence Factors of Immunological Interest
Index 417
