Immunoinformatics Predicting Immunogenicity In Silico by Darren R. Flower , Humana Press Inc.

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Humana Press Inc. Immunoinformatics Predicting Immunogenicity In Silico by Darren R. Flower

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._x000D_ _x000D_ Immunogenicity:_x000D_ Predicting Immunogenicity in silico_x000D_ _x000D_ [NOTE: As these papers describe computational methods, NONE are in the_x000D_ strict MiMB format, though most approximate it. This I have discussed_x000D_ with John Walker, and he indicates that this is acceptable. I indicate_x000D_ below those papers which do not even have a MiMB-like format.]_x000D_ _x000D_ 0. Preface_x000D_ [THIS IS NOT IN MiMB FORMAT]_x000D_ _x000D_ 1. Immunoinformatics and the in silico prediction of Immunogenicity:_x000D_ An introduction._x000D_ Darren R Flower_x000D_ [THIS IS NOT IN MiMB FORMAT]_x000D_ _x000D_ Section 1: Databases_x000D_ _x000D_ 2. IMGT (R), the international ImMunoGeneTics information system (R) for_x000D_ immunoinformatics. Methods for querying IMGT (R) databases, tools and Web_x000D_ resources in the context of immunoinformatics_x000D_ Marie-Paule Lefranc_x000D_ _x000D_ [Prof LeFranc has agreed to pay for colour figures, but needs to be_x000D_ billed.]_x000D_ _x000D_ 3. The IMGT/HLA Database_x000D_ James Robinson and Steven G. E. Marsh_x000D_ 4. IPD - the Immuno Polymorphism Database_x000D_ James Robinson and Steven G. E. Marsh_x000D_ _x000D_ 5. SYFPEITHI: Database for Searching and T-Cell Epitope Prediction_x000D_ Mathias M. Schuler, Maria-Dorothea Nastke and Stefan Stevanovi__x000D_ _x000D_ 6. Searching and Mapping of T cell epitopes, MHC binders, and TAP_x000D_ binders_x000D_ Manoj Bhasin, Sneh Lata and Gajendra P S Raghava_x000D_ _x000D_ 7. Searching and Mapping of B-cell epitopes in Bcipep database_x000D_ Sudipto Saha and Gajendra P.S. Raghava_x000D_ _x000D_ 8. Searching haptens, carrier proteins and anti-hapten antibodies_x000D_ Shilpy Srivastava, Mahender Kumar Singh, Gajendra P S Raghava_x000D_ and G. C. Varshney_x000D_ _x000D_ Section 2: Defining HLA Supertypes_x000D_ _x000D_ 9. The classification of HLA supertypes by GRID/CPCA_x000D_ and hierarchical clustering methods_x000D_ Pingping Guan, Irini A. Doytchinova and Darren R. Flower_x000D_ _x000D_ 10. Structural Basis For Hla-A2 Supertypes_x000D_ Pandjassarame Kangueane and Meena Kishore Sakharkar_x000D_ _x000D_ 11. Definition of MHC Supertypes Through Clustering of_x000D_ MHC Peptide-bindingRepertoires_x000D_ Pedro A. Reche and Ellis L. Reinherz_x000D_ _x000D_ 12. Grouping Of Class I Hla Alleles Using Electrostatic Distribution_x000D_ Maps_x000D_ Of The Peptide Binding Grooves._x000D_ Pandjassarame Kangueane and Meena Kishore Sakharkar_x000D_ _x000D_ Section 3: Predicting peptide-MHC binding_x000D_ _x000D_ 13. Predicton of Peptide-MHC Binding Using Profiles_x000D_ Pedro A. Reche and Ellis L. Reinherz_x000D_ _x000D_ 14. Application of machine learning techniques in predicting MHC binders_x000D_ Sneh Lata, Manoj Bhasin and G P S Raghava_x000D_ _x000D_ 15. Artificial Intelligence Methods for Predicting T-Cell Epitopes_x000D_ Yingdong Zhao, Myong-Hee Sung, Richard Simon_x000D_ _x000D_ 16. Towards the Prediction of Class I and II Mouse Major_x000D_ Histocompatibility_x000D_ Complex Peptide Binding Affinity: In Silico Bioinformatic Step by Step_x000D_ Guide Using Quantitative Structure-Activity Relationships_x000D_ Channa K. Hattotuwagama, Irini A. Doytchinova, & Darren R. Flower_x000D_ _x000D_ 17. Predicting the MHC-peptide affinity using some interactive type_x000D_ molecular descriptors and QSAR models_x000D_ Thy-Hou Lin_x000D_ _x000D_ 18. Implementing the Modular MHC Model for Predicting Peptide Binding_x000D_ David S. DeLuca and Rainer Blasczyk_x000D_ _x000D_ 19. Support vector machine-based prediction of MHC binding peptides_x000D_ Pierre Doennes _x000D_ _x000D_ 20. In silico prediction of peptide MHC binding affinity using SVRMHC_x000D_ Wen Liu, Ji Wan, Xiangshan Meng, Darren R. Flower and Tongbin Li_x000D_ _x000D_ 21. HLA-Peptide Binding Prediction Using Structural And Modeling_x000D_ Principles_x000D_ Pandjassarame Kangueane and Meena Kishore Sakharkar_x000D_ _x000D_ 22. A Practical Guide to Structure-based Prediction of MHC Binding_x000D_ Peptides_x000D_ Shoba Ranganathan and Joo Chuan Tong_x000D_ _x000D_ 23. Static Energy Analysis of MHC Class I and Class II-peptide binding_x000D_ affinity_x000D_ Matthew N. Davies and Darren R. Flower_x000D_ _x000D_ 24. Molecular dynamics simulations:_x000D_ bring biomolecular structures alive on a computer_x000D_ Shunzhou Wan, Peter V. Coveney, & Darren R. Flower_x000D_ _x000D_ 25. An Iterative Approach to Class II_x000D_