Immunoinformatics: Predicting Immunogenicity In Silico

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Springer Science & Business Media, Jun 21, 2007 - Computers - 438 pages
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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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Contents

Immunoinformatics and the In Silico Prediction of Immunogenicity An Introduction
1
Databases
16
IMGT the International ImmunoGeneTics Information System for Immunoinformatics Methods for Querying IMGT Databases Tools and Web Reso...
19
The IMGTHLA Database
43
IPD The Immuno Polymorphism Database
61
SYFPEITHI Database for Searching and TCell Epitope Prediction
75
Searching and Mapping of TCell Epitopes MHC Binders and TAP Binders
95
Searching and Mapping of BCell Epitopes in Bcipep Database
113
Predicting the MHCPeptide Affinity Using Some InteractiveType Molecular Descriptors and QSAR Models
247
Implementing the Modular MHC Model for Predicting Peptide Binding
261
Support Vector MachineBased Prediction of MHCBinding Peptides
273
In Silico Prediction of PeptideMHC Binding Affinity Using SVRMHC
283
HLAPeptide Binding Prediction Using Structural and Modeling Principles
293
A Practical Guide to StructureBased Prediction of MHCBinding Peptides
301
Static Energy Analysis of MHC Class I and Class II PeptideBinding Affinity
309
Molecular Dynamics Simulations Bring Biomolecular Structures Alive on a Computer
321

Searching Haptens Carrier Proteins and AntiHapten Antibodies
125
Defining HLA Supertypes
142
The Classification of HLA Supertypes by GRIDCPCA and Hierarchical Clustering Methods
143
Structural Basis for HLAA2 Supertypes
155
Definition of MHC Supertypes Through Clustering of MHC PeptideBinding Repertoires
163
Grouping of Class I HLA Alleles Using Electrostatic Distribution Maps of the Peptide Binding Grooves
175
Predicting PeptideMHC Binding
182
Prediction of PeptideMHC Binding Using Profiles
185
Application of Machine Learning Techniques in Predicting MHC Binders
201
Artificial Intelligence Methods for Predicting TCell Epitopes
217
Toward the Prediction of Class I and II Mouse Major Histocompatibility ComplexPeptideBinding Affinity In Silico Bioinformatic StepbyStep Guide ...
227
An Iterative Approach to Class II Predictions
341
Building a MetaPredictor for MHC Class IIBinding Peptides
355
Nonlinear Predictive Modeling of MHC Class IIPeptide Binding Using Bayesian Neural Networks
365
Predicting other Properties of Immune Systems
378
TAPPred Prediction of TAPBinding Peptides in Antigens
381
Prediction Methods for Bcell Epitopes
387
HistoCheck Evaluating Structural and Functional MHC Similarities
395
Predicting Virulence Factors of Immunological Interest
407
Index
417
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