Immunoinformatics: Predicting Immunogenicity In Silico is a primer for researchers interested in this emerging and exciting techno...
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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.
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 Inhalt 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