title: Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens.
abstract: BACKGROUND
  Processing and presentation of major histocompatibility complex class I antigens to cytotoxic T-lymphocytes is crucial for immune surveillance against intracellular bacteria, parasites, viruses and tumors. Identification of antigenic regions on pathogen proteins will play a pivotal role in designer vaccine immunotherapy. We have developed a system that not only identifies high binding T-cell antigenic epitopes, but also class I T-cell antigenic clusters termed immunological hot spots.
  
  
  METHODS
  MULTIPRED, a computational system for promiscuous prediction of HLA class I binders, uses artificial neural networks (ANN) and hidden Markov models (HMM) as predictive engines. The models were rigorously trained, tested and validated using experimentally identified HLA class I T-cell epitopes from human melanoma related proteins and human papillomavirus proteins E6 and E7. We have developed a scoring scheme for identification of immunological hot spots for HLA class I molecules, which is the sum of the highest four predictions within a window of 30 amino acids.
  
  
  RESULTS
  Our predictions against experimental data from four melanoma-related proteins showed that MULTIPRED ANN and HMM models could predict T-cell epitopes with high accuracy. The analysis of proteins E6 and E7 showed that ANN models appear to be more accurate for prediction of HLA-A3 hot spots and HMM models for HLA-A2 predictions. For illustration of its utility we applied MULTIPRED for prediction of promiscuous T-cell epitopes in all four SARS coronavirus structural proteins. MULTIPRED predicted HLA-A2 and HLA-A3 hot spots in each of these proteins.
authors.affiliation: Division of Biomedical Sciences, Johns Hopkins Medicine in Singapore, The Gemini, Singapore.
authors.forename: K N
authors.initials: KN
authors.lastname: Srinivasan
authors.forename: G L
authors.initials: GL
authors.lastname: Zhang
authors.forename: A M
authors.initials: AM
authors.lastname: Khan
authors.forename: J T
authors.initials: JT
authors.lastname: August
authors.forename: V
authors.initials: V
authors.lastname: Brusic
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D018604:Epitope Mapping / Q000379:methods*; D018984:Epitopes, T-Lymphocyte / Q000276:immunology*; D015395:Histocompatibility Antigens Class I / Q000276:immunology*; D018448:Models, Immunological; D009856:Oncogene Proteins, Viral / Q000276:immunology*; D050725:Papillomavirus E7 Proteins / Q000276:immunology*; D010455:Peptides / Q000276:immunology / Q000378:metabolism; D011485:Protein Binding
chemical_list: C118860:E6 protein, Human papillomavirus type 6; D018984:Epitopes, T-Lymphocyte; D015395:Histocompatibility Antigens Class I; D009856:Oncogene Proteins, Viral; D050725:Papillomavirus E7 Proteins; D010455:Peptides