Immunoinformatics

Immunoinformatics: Predicting Immunogenicity In Silico is a primer for researchers interested in this emerging and exciting technology and provides examples in the major areas within the field of immunoinformatics. This volume both engages the reader and provides a sound foundation for the use of immunoinformatics techniques in immunology and vaccinology. The volume is conveniently divided into four sections. The first section, Databases, details various immunoinformatic databases, including IMGT/HLA, IPD, and SYEPEITHI. In the second section, Defining HLA Supertypes, authors discuss supertypes of GRID/CPCA and hierarchical clustering methods, Hla-Ad supertypes, MHC supertypes, and Class I Hla Alleles. The third section, Predicting Peptide-MCH Binding, includes discussions of MCH binders, T-Cell epitopes, Class I and II Mouse Major Histocompatibility, and HLA-peptide binding. Within the fourth section, Predicting Other Properties of Immune Systems, investigators outline TAP binding, B-cell epitopes, MHC similarities, and predicting virulence factors of immunological interest. Immunoinformatics: Predicting Immunogenicity In Silico merges skill sets of the lab-based and the computer-based science professional into one easy-to-use, insightful volume.

Preface	p. v
Contributors	p. xi
Color Plates	p. xv
Immunoinformatics and the In Silico Prediction of Immunogenicity: An Introduction	p. 1
Databases
IMGT, the International ImmunoGeneTics Information System for Immunoinformatics: Methods for Querying IMGT Databases, Tools, and Web Resources in the Context of Immunoinformatics	p. 19
The IMGT/HLA Database	p. 43
IPD: The Immuno Polymorphism Database	p. 61
SYFPEITHI: Database for Searching and T-Cell Epitope Prediction	p. 75
Searching and Mapping of T-Cell Epitopes, MHC Binders, and TAP Binders	p. 95
Searching and Mapping of B-Cell Epitopes in Bcipep Database	p. 113
Searching Haptens, Carrier Proteins, and Anti-Hapten Antibodies	p. 125
Defining HLA Supertypes
The Classification of HLA Supertypes by GRID/CPCA and Hierarchical Clustering Methods	p. 143
Structural Basis for HLA-A2 Supertypes	p. 155
Definition of MHC Supertypes Through Clustering of MHC Peptide-Binding Repertoires	p. 163
Grouping of Class I HLA Alleles Using Electrostatic Distribution Maps of the Peptide Binding Grooves	p. 175
Predicting Peptide-MHC Binding
Prediction of Peptide-MHC Binding Using Profiles	p. 185
Application of Machine Learning Techniques in Predicting MHC Binders	p. 201
Artificial Intelligence Methods for Predicting T-Cell Epitopes	p. 217
Toward the Prediction of Class I and II Mouse Major Histocompatibility Complex-Peptide-Binding Affinity: In Silico Bioinformatic Step-by-Step Guide Using Quantitative Structure-Activity Relationships	p. 227
Predicting the MHC-Peptide Affinity Using Some Interactive-Type Molecular Descriptors and QSAR Models	p. 247
Implementing the Modular MHC Model for Predicting Peptide Binding	p. 261
Support Vector Machine-Based Prediction of MHC-Binding Peptides	p. 273
In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC	p. 283
HLA-Peptide Binding Prediction Using Structural and Modeling Principles	p. 293
A Practical Guide to Structure-Based Prediction of MHC-Binding Peptides	p. 301
Static Energy Analysis of MHC Class I and Class II Peptide-Binding Affinity	p. 309
Molecular Dynamics Simulations: Bring Biomolecular Structures Alive on a Computer	p. 321
An Iterative Approach to Class II Predictions	p. 341
Building a Meta-Predictor for MHC Class II-Binding Peptides	p. 355
Nonlinear Predictive Modeling of MHC Class II-Peptide Binding Using Bayesian Neural Networks	p. 365
Predicting other Properties of Immune Systems
TAPPred Prediction of TAP-Binding Peptides in Antigens	p. 381
Prediction Methods for B-cell Epitopes	p. 387
HistoCheck	p. 395
Predicting Virulence Factors of Immunological Interest	p. 407
Index	p. 417
Table of Contents provided by Ingram. All Rights Reserved.

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