Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens
Identifieur interne : 000445 ( Istex/Curation ); précédent : 000444; suivant : 000446Prediction 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.
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DOI: 10.1093/bioinformatics/bth943
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K. N. Srinivasan<affiliation><mods:affiliation>Division of Biomedical Sciences, Johns Hopkins Medicine in Singapore, 41-Science Park Road, Lobby C, The Gemini, Singapore 117610,</mods:affiliation><wicri:noCountry code="subField"></wicri:noCountry></affiliation><affiliation><mods:affiliation>Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613,</mods:affiliation><wicri:noCountry code="subField"></wicri:noCountry></affiliation><affiliation><mods:affiliation>Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613,</mods:affiliation><wicri:noCountry code="subField"></wicri:noCountry></affiliation>Le document en format XML
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Zhang</name><affiliation><mods:affiliation>Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613,</mods:affiliation><wicri:noCountry code="subField"></wicri:noCountry></affiliation></author><author><name sortKey="Khan, A M" sort="Khan, A M" uniqKey="Khan A" first="A. M." last="Khan">A. M. Khan</name><affiliation><mods:affiliation>Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613,</mods:affiliation><wicri:noCountry code="subField"></wicri:noCountry></affiliation></author><author><name sortKey="August, J T" sort="August, J T" uniqKey="August J" first="J. T." last="August">J. T. 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N." last="Srinivasan">K. N. Srinivasan</name><affiliation><mods:affiliation>Division of Biomedical Sciences, Johns Hopkins Medicine in Singapore, 41-Science Park Road, Lobby C, The Gemini, Singapore 117610,</mods:affiliation><wicri:noCountry code="subField"></wicri:noCountry></affiliation></author><author><name sortKey="Zhang, G L" sort="Zhang, G L" uniqKey="Zhang G" first="G. L." last="Zhang">G. L. Zhang</name><affiliation><mods:affiliation>Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613,</mods:affiliation><wicri:noCountry code="subField"></wicri:noCountry></affiliation></author><author><name sortKey="Khan, A M" sort="Khan, A M" uniqKey="Khan A" first="A. M." last="Khan">A. M. Khan</name><affiliation><mods:affiliation>Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613,</mods:affiliation><wicri:noCountry code="subField"></wicri:noCountry></affiliation></author><author><name sortKey="August, J T" sort="August, J T" uniqKey="August J" first="J. T." last="August">J. T. 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Brusic</name><affiliation><mods:affiliation>Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613,</mods:affiliation></affiliation><affiliation wicri:level="4"><mods:affiliation>Department of Microbiology, National University of Singapore, 5 Science Drive 2, Singapore 117597</mods:affiliation><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></record>Pour manipuler ce document sous Unix (Dilib)
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