- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
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.
Contents
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 otherProperties 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.
