Immunoinformatics: Predicting Immunogenicity In Silico
Darren R. Flower
Springer Science & Business Media, Jun 21, 2007 - Computers - 438 pages
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.
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Immunoinformatics and the In Silico Prediction of Immunogenicity An Introduction
IMGT the International ImmunoGeneTics Information System for Immunoinformatics Methods for Querying IMGT Databases Tools and Web Reso...
The IMGTHLA Database
IPD The Immuno Polymorphism Database
SYFPEITHI Database for Searching and TCell Epitope Prediction
Searching and Mapping of TCell Epitopes MHC Binders and TAP Binders
Searching and Mapping of BCell Epitopes in Bcipep Database
Predicting the MHCPeptide Afﬁnity Using Some InteractiveType Molecular Descriptors and QSAR Models
Implementing the Modular MHC Model for Predicting Peptide Binding
Support Vector MachineBased Prediction of MHCBinding Peptides
In Silico Prediction of PeptideMHC Binding Afﬁnity Using SVRMHC
HLAPeptide Binding Prediction Using Structural and Modeling Principles
A Practical Guide to StructureBased Prediction of MHCBinding Peptides
Static Energy Analysis of MHC Class I and Class II PeptideBinding Afﬁnity
Molecular Dynamics Simulations Bring Biomolecular Structures Alive on a Computer
Searching Haptens Carrier Proteins and AntiHapten Antibodies
Deﬁning HLA Supertypes
The Classiﬁcation of HLA Supertypes by GRIDCPCA and Hierarchical Clustering Methods
Structural Basis for HLAA2 Supertypes
Deﬁnition of MHC Supertypes Through Clustering of MHC PeptideBinding Repertoires
Grouping of Class I HLA Alleles Using Electrostatic Distribution Maps of the Peptide Binding Grooves
Predicting PeptideMHC Binding
Prediction of PeptideMHC Binding Using Proﬁles
Application of Machine Learning Techniques in Predicting MHC Binders
Artiﬁcial Intelligence Methods for Predicting TCell Epitopes
Toward the Prediction of Class I and II Mouse Major Histocompatibility ComplexPeptideBinding Afﬁnity In Silico Bioinformatic StepbyStep Guide ...
An Iterative Approach to Class II Predictions
Building a MetaPredictor for MHC Class IIBinding Peptides
Nonlinear Predictive Modeling of MHC Class IIPeptide Binding Using Bayesian Neural Networks
Predicting other Properties of Immune Systems
TAPPred Prediction of TAPBinding Peptides in Antigens
Prediction Methods for Bcell Epitopes
HistoCheck Evaluating Structural and Functional MHC Similarities
Predicting Virulence Factors of Immunological Interest