Immunoinformatics: Predicting Immunogenicity In Silico

Immunogenicity:Predicting Immunogenicity in silico[NOTE: As these papers describe computational methods, NONE are in thestrict MiMB format, though most approximate it. This I have discussedwith 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: Databases2. IMGT®, the international ImMunoGeneTics information system® forimmunoinformatics. Methods for querying IMGT® databases, tools and Webresources in the context of immunoinformaticsMarie-Paule Lefranc[Prof LeFranc has agreed to pay for colour figures, but needs to bebilled.]3. The IMGT/HLA DatabaseJames Robinson and Steven G. E. Marsh4. IPD - the Immuno Polymorphism DatabaseJames Robinson and Steven G. E. Marsh5. SYFPEITHI: Database for Searching and T-Cell Epitope PredictionMathias M. Schuler, Maria-Dorothea Nastke and Stefan Stevanovi_6. Searching and Mapping of T cell epitopes, MHC binders, and TAPbindersManoj Bhasin, Sneh Lata and Gajendra P S Raghava7. Searching and Mapping of B-cell epitopes in Bcipep databaseSudipto Saha and Gajendra P.S. Raghava8. Searching haptens, carrier proteins and anti-hapten antibodiesShilpy Srivastava, Mahender Kumar Singh, Gajendra P S Raghavaand G. C. VarshneySection 2: Defining HLA Supertypes9. The classification of HLA supertypes by GRID/CPCAand hierarchical clustering methodsPingping Guan, Irini A. Doytchinova and Darren R. Flower10. Structural Basis For Hla-A2 SupertypesPandjassarame Kangueane and Meena Kishore Sakharkar11. Definition of MHC Supertypes Through Clustering ofMHC Peptide-bindingRepertoiresPedro A. Reche and Ellis L. Reinherz12. Grouping Of Class I Hla Alleles Using Electrostatic DistributionMapsOf The Peptide Binding Grooves.Pandjassarame Kangueane and Meena Kishore SakharkarSection 3: Predicting peptide-MHC binding13. Predicton of Peptide-MHC Binding Using ProfilesPedro A. Reche and Ellis L. Reinherz14. Application of machine learning techniques in predicting MHC bindersSneh Lata, Manoj Bhasin and G P S Raghava15. Artificial Intelligence Methods for Predicting T-Cell EpitopesYingdong Zhao, Myong-Hee Sung, Richard Simon16. Towards the Prediction of Class I and II Mouse MajorHistocompatibilityComplex Peptide Binding Affinity: In Silico Bioinformatic Step by StepGuide Using Quantitative Structure-Activity RelationshipsChanna K. Hattotuwagama, Irini A. Doytchinova, & Darren R. Flower17. Predicting the MHC-peptide affinity using some interactive typemolecular descriptors and QSAR modelsThy-Hou Lin18. Implementing the Modular MHC Model for Predicting Peptide BindingDavid S. DeLuca and Rainer Blasczyk19. Support vector machine-based prediction of MHC binding peptidesPierre Dönnes 20. In silico prediction of peptide MHC binding affinity using SVRMHCWen Liu, Ji Wan, Xiangshan Meng, Darren R. Flower and Tongbin Li21. HLA-Peptide Binding Prediction Using Structural And ModelingPrinciplesPandjassarame Kangueane and Meena Kishore Sakharkar22. A Practical Guide to Structure-based Prediction of MHC BindingPeptidesShoba Ranganathan and Joo Chuan Tong23. Static Energy Analysis of MHC Class I and Class II-peptide bindingaffinityMatthew N. Davies and Darren R. Flower24. Molecular dynamics simulations:bring biomolecular structures alive on a computerShunzhou Wan, Peter V. Coveney, & Darren R. Flower25. An Iterative Approach to Class II