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MORE ABOUT THIS BOOK
Main 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.
Feature:
Addresses databases, HLA supertypes, MCH binding, and other properties of immune systems
A firm background for anyone working in immunoinformatics
Chapters written by leaders in the field
Contents:
Immunogenicity:
Predicting Immunogenicity in silico
[NOTE: As these papers describe computational methods, NONE are in the
strict MiMB format, though most approximate it. This I have discussed
with John Walker, and he indicates that this is acceptable. I indicate
below those papers which do not even have a MiMB-like format.]
0. Preface
[THIS IS NOT IN MiMB FORMAT]
1. Immunoinformatics and the in silico prediction of Immunogenicity:
An introduction.
Darren R Flower
[THIS IS NOT IN MiMB FORMAT]
Section 1: Databases
2. IMGT®, the international ImMunoGeneTics information system® for
immunoinformatics. Methods for querying IMGT® databases, tools and Web
resources in the context of immunoinformatics
Marie-Paule Lefranc
[Prof LeFranc has agreed to pay for colour figures, but needs to be
billed.]
3. The IMGT/HLA Database
James Robinson and Steven G. E. Marsh
4. IPD - the Immuno Polymorphism Database
James Robinson and Steven G. E. Marsh
5. SYFPEITHI: Database for Searching and T-Cell Epitope Prediction
Mathias M. Schuler, Maria-Dorothea Nastke and Stefan Stevanovi_
6. Searching and Mapping of T cell epitopes, MHC binders, and TAP
binders
Manoj Bhasin, Sneh Lata and Gajendra P S Raghava
7. Searching and Mapping of B-cell epitopes in Bcipep database
Sudipto Saha and Gajendra P.S. Raghava
8. Searching haptens, carrier proteins and anti-hapten antibodies
Shilpy Srivastava, Mahender Kumar Singh, Gajendra P S Raghava
and G. C. Varshney
Section 2: Defining HLA Supertypes
9. The classification of HLA supertypes by GRID/CPCA
and hierarchical clustering methods
Pingping Guan, Irini A. Doytchinova and Darren R. Flower
10. Structural Basis For Hla-A2 Supertypes
Pandjassarame Kangueane and Meena Kishore Sakharkar
11. Definition of MHC Supertypes Through Clustering of
MHC Peptide-bindingRepertoires
Pedro A. Reche and Ellis L. Reinherz
12. Grouping Of Class I Hla Alleles Using Electrostatic Distribution
Maps
Of The Peptide Binding Grooves.
Pandjassarame Kangueane and Meena Kishore Sakharkar
Section 3: Predicting peptide-MHC binding
13. Predicton of Peptide-MHC Binding Using Profiles
Pedro A. Reche and Ellis L. Reinherz
14. Application of machine learning techniques in predicting MHC binders
Sneh Lata, Manoj Bhasin and G P S Raghava
15. Artificial Intelligence Methods for Predicting T-Cell Epitopes
Yingdong Zhao, Myong-Hee Sung, Richard Simon
16. Towards 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
Channa K. Hattotuwagama, Irini A. Doytchinova, & Darren R. Flower
17. Predicting the MHC-peptide affinity using some interactive type
molecular descriptors and QSAR models
Thy-Hou Lin
18. Implementing the Modular MHC Model for Predicting Peptide Binding
David S. DeLuca and Rainer Blasczyk
19. Support vector machine-based prediction of MHC binding peptides
Pierre Dönnes
20. In silico prediction of peptide MHC binding affinity using SVRMHC
Wen Liu, Ji Wan, Xiangshan Meng, Darren R. Flower and Tongbin Li
21. HLA-Peptide Binding Prediction Using Structural And Modeling
Principles
Pandjassarame Kangueane and Meena Kishore Sakharkar
22. A Practical Guide to Structure-based Prediction of MHC Binding
Peptides
Shoba Ranganathan and Joo Chuan Tong
23. Static Energy Analysis of MHC Class I and Class II-peptide binding
affinity
Matthew N. Davies and Darren R. Flower
24. Molecular dynamics simulations:
bring biomolecular structures alive on a computer
Shunzhou Wan, Peter V. Coveney, & Darren R. Flower
25. An Iterative Approach to Class II
PRODUCT DETAILS
Publisher: Springer (Humana Press)
Publication date: June, 2007
Pages: 438
Weight: 847g
Availability: Not available (reason unspecified)
Subcategories: Genetics, Immunology
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CUSTOMER REVIEWS
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"...a value to virtually any investigator in this general field." -Doody's Book Review, Weighted Numerical Score:77 - 3 Stars
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