A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes.
Identifieur interne : 001A07 ( Main/Exploration ); précédent : 001A06; suivant : 001A08A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes.
Auteurs : Xue Wu Zhang [République populaire de Chine]Source :
- Computational biology and chemistry [ 1476-928X ] ; 2013.
Descripteurs français
- KwdFr :
- Algorithmes, Antigènes d'histocompatibilité de classe I (), Antigènes d'histocompatibilité de classe I (immunologie), Biologie informatique, Déterminants antigéniques des lymphocytes T (), Déterminants antigéniques des lymphocytes T (immunologie), Humains, Lymphocytes T (), Lymphocytes T (immunologie), Protéine de capside p24 du VIH (), Protéine de capside p24 du VIH (immunologie), Protéines de la matrice virale (), Protéines de la matrice virale (immunologie), Simulation de docking moléculaire.
- MESH :
- immunologie : Antigènes d'histocompatibilité de classe I, Déterminants antigéniques des lymphocytes T, Lymphocytes T, Protéine de capside p24 du VIH, Protéines de la matrice virale.
- Algorithmes, Antigènes d'histocompatibilité de classe I, Biologie informatique, Déterminants antigéniques des lymphocytes T, Humains, Lymphocytes T, Protéine de capside p24 du VIH, Protéines de la matrice virale, Simulation de docking moléculaire.
English descriptors
- KwdEn :
- Algorithms, Computational Biology, Epitopes, T-Lymphocyte (chemistry), Epitopes, T-Lymphocyte (immunology), HIV Core Protein p24 (chemistry), HIV Core Protein p24 (immunology), Histocompatibility Antigens Class I (chemistry), Histocompatibility Antigens Class I (immunology), Humans, Molecular Docking Simulation, T-Lymphocytes (chemistry), T-Lymphocytes (immunology), Viral Matrix Proteins (chemistry), Viral Matrix Proteins (immunology).
- MESH :
- chemical , chemistry : Epitopes, T-Lymphocyte, HIV Core Protein p24, Histocompatibility Antigens Class I, Viral Matrix Proteins.
- chemical , immunology : Epitopes, T-Lymphocyte, HIV Core Protein p24, Histocompatibility Antigens Class I, Viral Matrix Proteins.
- chemistry : T-Lymphocytes.
- immunology : T-Lymphocytes.
- Algorithms, Computational Biology, Humans, Molecular Docking Simulation.
Abstract
In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.
DOI: 10.1016/j.compbiolchem.2013.03.003
PubMed: 23666426
Affiliations:
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Le document en format XML
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class I restricted T-cell epitopes.</title><author><name sortKey="Zhang, Xue Wu" sort="Zhang, Xue Wu" uniqKey="Zhang X" first="Xue Wu" last="Zhang">Xue Wu Zhang</name><affiliation wicri:level="3"><nlm:affiliation>College of Light Industry and Food Sciences, South China University of Technology, Guangzhou, China. snow_dance@sina.com</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>College of Light Industry and Food Sciences, South China University of Technology, Guangzhou</wicri:regionArea><placeName><settlement type="city">Jiangmen</settlement><region type="province">Guangdong</region></placeName></affiliation></author></analytic><series><title level="j">Computational biology and chemistry</title><idno type="eISSN">1476-928X</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Computational Biology</term><term>Epitopes, T-Lymphocyte (chemistry)</term><term>Epitopes, T-Lymphocyte (immunology)</term><term>HIV Core Protein p24 (chemistry)</term><term>HIV Core Protein p24 (immunology)</term><term>Histocompatibility Antigens Class I (chemistry)</term><term>Histocompatibility Antigens Class I (immunology)</term><term>Humans</term><term>Molecular Docking Simulation</term><term>T-Lymphocytes (chemistry)</term><term>T-Lymphocytes (immunology)</term><term>Viral Matrix Proteins (chemistry)</term><term>Viral Matrix Proteins (immunology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Antigènes d'histocompatibilité de classe I ()</term><term>Antigènes d'histocompatibilité de classe I (immunologie)</term><term>Biologie informatique</term><term>Déterminants antigéniques des lymphocytes T ()</term><term>Déterminants antigéniques des lymphocytes T (immunologie)</term><term>Humains</term><term>Lymphocytes T ()</term><term>Lymphocytes T (immunologie)</term><term>Protéine de capside p24 du VIH ()</term><term>Protéine de capside p24 du VIH (immunologie)</term><term>Protéines de la matrice virale ()</term><term>Protéines de la matrice virale (immunologie)</term><term>Simulation de docking moléculaire</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Epitopes, T-Lymphocyte</term><term>HIV Core Protein p24</term><term>Histocompatibility Antigens Class I</term><term>Viral Matrix Proteins</term></keywords><keywords scheme="MESH" type="chemical" qualifier="immunology" xml:lang="en"><term>Epitopes, T-Lymphocyte</term><term>HIV Core Protein p24</term><term>Histocompatibility Antigens Class I</term><term>Viral Matrix Proteins</term></keywords><keywords scheme="MESH" qualifier="chemistry" xml:lang="en"><term>T-Lymphocytes</term></keywords><keywords scheme="MESH" qualifier="immunologie" xml:lang="fr"><term>Antigènes d'histocompatibilité de classe I</term><term>Déterminants antigéniques des lymphocytes T</term><term>Lymphocytes T</term><term>Protéine de capside p24 du VIH</term><term>Protéines de la matrice virale</term></keywords><keywords scheme="MESH" qualifier="immunology" xml:lang="en"><term>T-Lymphocytes</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Computational Biology</term><term>Humans</term><term>Molecular Docking Simulation</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Antigènes d'histocompatibilité de classe I</term><term>Biologie informatique</term><term>Déterminants antigéniques des lymphocytes T</term><term>Humains</term><term>Lymphocytes T</term><term>Protéine de capside p24 du VIH</term><term>Protéines de la matrice virale</term><term>Simulation de docking moléculaire</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Guangdong</li></region><settlement><li>Jiangmen</li></settlement></list><tree><country name="République populaire de Chine"><region name="Guangdong"><name sortKey="Zhang, Xue Wu" sort="Zhang, Xue Wu" uniqKey="Zhang X" first="Xue Wu" last="Zhang">Xue Wu Zhang</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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