Databases -- IMGT®, the International ImmunoGeneTics Information System® for Immunoinformatics -- The IMGT/HLA Database -- IPD -- SYFPEITHI -- Searching and Mapping of T-Cell Epitopes, MHC Binders, and TAP Binders -- Searching and Mapping of B-Cell Epitopes in Bcipep Database -- Searching Haptens, Carrier Proteins, and Anti-Hapten Antibodies -- Defining HLA Supertypes -- The Classification of HLA Supertypes by GRID/CPCA and Hierarchical Clustering Methods -- Structural Basis for HLA-A2 Supertypes -- Definition of MHC Supertypes Through Clustering of MHC Peptide-Binding Repertoires -- Grouping of Class I HLA Alleles Using Electrostatic Distribution Maps of the Peptide Binding Grooves -- Predicting Peptide-MHC Binding -- Prediction of Peptide-MHC Binding Using Profiles -- Application of Machine Learning Techniques in Predicting MHC Binders -- Artificial Intelligence Methods for Predicting T-Cell Epitopes -- Toward the Prediction of Class I and II Mouse Major Histocompatibility Complex-Peptide-Binding Affinity -- Predicting the MHC-Peptide Affinity Using Some Interactive-Type Molecular Descriptors and QSAR Models -- Implementing the Modular MHC Model for Predicting Peptide Binding -- Support Vector Machine-Based Prediction of MHC-Binding Peptides -- In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC -- HLA-Peptide Binding Prediction Using Structural and Modeling Principles -- A Practical Guide to Structure-Based Prediction of MHC-Binding Peptides -- Static Energy Analysis of MHC Class I and Class II Peptide-Binding Affinity -- Molecular Dynamics Simulations -- An Iterative Approach to Class II Predictions -- Building a Meta-Predictor for MHC Class II-Binding Peptides -- Nonlinear Predictive Modeling of MHC Class II-Peptide Binding Using Bayesian Neural Networks -- Predicting other Properties of Immune Systems -- TAPPred Prediction of TAP-Binding Peptides in Antigens -- Prediction Methods for B-cell Epitopes -- HistoCheck -- Predicting Virulence Factors of Immunological Interest -- Immunoinformatics and the in Silico Prediction of Immunogenicity -- Immunoinformatics and the in Silico Prediction of Immunogenicity.

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.