상세정보

This volume both engages the reader and provides a sound foundation for the use of immunoinformatics techniques in immunology and vaccinology. It addresses databases, HLA supertypes, MCH binding, and other properties of immune systems. The book contains chapters written by leaders in the field and provides a firm background for anyone working in immunoinformatics in one easy-to-use, insightful volume.

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.

정보제공 :

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 indicatebelow 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 Donnes

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

정보제공 :