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Title: Prediction of class I T-cell epitopes: Evidence of presence of immunological hot spots inside antigens
Authors: Srinivasan, K.N. 
Zhang, G.L.
Khan, A.M.
August, J.T. 
Brusic, V. 
Issue Date: 2004
Citation: Srinivasan, K.N., Zhang, G.L., Khan, A.M., August, J.T., Brusic, V. (2004). Prediction of class I T-cell epitopes: Evidence of presence of immunological hot spots inside antigens. Bioinformatics 20 (SUPPL. 1) : i297-i302. ScholarBank@NUS Repository.
Abstract: 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. © Oxford University Press 2004; all rights reserved.
Source Title: Bioinformatics
ISSN: 13674803
DOI: 10.1093/bioinformatics/bth943
Appears in Collections:Staff Publications

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