Bioinformatics 20(Suppl. 1) © Oxford University Press 2004; all rights reserved.
Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens
1 Division of Biomedical Sciences, Johns Hopkins Medicine in Singapore, 41-Science Park Road, Lobby C, The Gemini, Singapore 117610, 2 Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, 3 Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA and 4 Department of Microbiology, National University of Singapore, 5 Science Drive 2, Singapore 117597
Received on January 15, 2004; accepted on March 1, 2004
Motivation: 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.
Contact: vladimir{at}i2r.a-star.edu.sg
* To whom correspondence should be addressed.
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