Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens
Identifieur interne : 003127 ( Main/Exploration ); précédent : 003126; suivant : 003128Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens
Auteurs : K. N. Srinivasan ; G. L. Zhang ; A. M. Khan ; J. T. August [États-Unis] ; V. Brusic [Singapour]Source :
- Bioinformatics [ 1367-4803 ] ; 2004.
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.
Url:
DOI: 10.1093/bioinformatics/bth943
Affiliations:
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August</name><affiliation></affiliation><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD</wicri:regionArea><wicri:noRegion>MD</wicri:noRegion></affiliation></author><author><name sortKey="Brusic, V" sort="Brusic, V" uniqKey="Brusic V" first="V." last="Brusic">V. Brusic</name><affiliation></affiliation><affiliation wicri:level="4"><orgName type="university">Université nationale de Singapour</orgName><country>Singapour</country></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Bioinformatics</title><title level="j" type="issue">Proceedings Twelfth International Conference on Intelligent Systems for Molecular Biology/Third European Conference on Computational Biology 2004 Glasgow, UK July 31-August 4, 2004</title><title level="j" type="abbrev">Bioinformatics</title><idno type="ISSN">1367-4803</idno><idno type="eISSN">1460-2059</idno><imprint><publisher>Oxford University Press</publisher><date type="published">2004</date><biblScope unit="vol">20</biblScope><biblScope unit="issue">suppl_1</biblScope><biblScope unit="page" from="i297">i297</biblScope><biblScope unit="page" to="i302">i302</biblScope></imprint><idno type="ISSN">1367-4803</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1367-4803</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><affiliations><list><country><li>Singapour</li><li>États-Unis</li></country><orgName><li>Université nationale de Singapour</li></orgName></list><tree><noCountry><name sortKey="Khan, A M" sort="Khan, A M" uniqKey="Khan A" first="A. M." last="Khan">A. M. Khan</name><name sortKey="Srinivasan, K N" sort="Srinivasan, K N" uniqKey="Srinivasan K" first="K. N." last="Srinivasan">K. N. 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