Recovery of known T-cell epitopes by computational scanning of a viral genome
Identifieur interne : 003385 ( Main/Exploration ); précédent : 003384; suivant : 003386Recovery of known T-cell epitopes by computational scanning of a viral genome
Auteurs : Antoine Logean [France, Suisse] ; Didier Rognan [France]Source :
- Journal of Computer-Aided Molecular Design [ 0920-654X ] ; 2002-04-01.
Descripteurs français
- KwdFr :
- Allèles, Antigène HLA-A2, Antigène HLA-B27, Antigènes HLA-A (génétique), Antigènes HLA-A (métabolisme), Antigènes HLA-B (génétique), Antigènes HLA-B (métabolisme), Antigènes de l'hépatite virale B (génétique), Bases de données génétiques, Cartographie épitopique (), Génome viral, Humains, Liaison aux protéines, Logiciel, Lymphocytes T (immunologie), Motifs d'acides aminés, Séquence d'acides aminés, Techniques in vitro, Thermodynamique, Virus de l'hépatite B (génétique), Virus de l'hépatite B (immunologie), Épitopes immunodominants (génétique).
- MESH :
- génétique : Antigènes HLA-A, Antigènes HLA-B, Antigènes de l'hépatite virale B, Virus de l'hépatite B, Épitopes immunodominants.
- immunologie : Lymphocytes T, Virus de l'hépatite B.
- métabolisme : Antigènes HLA-A, Antigènes HLA-B.
- Allèles, Antigène HLA-A2, Antigène HLA-B27, Bases de données génétiques, Cartographie épitopique, Génome viral, Humains, Liaison aux protéines, Logiciel, Motifs d'acides aminés, Séquence d'acides aminés, Techniques in vitro, Thermodynamique.
English descriptors
- KwdEn :
- Alleles, Amino Acid Motifs, Amino Acid Sequence, Databases, Genetic, Epitope Mapping (statistics & numerical data), Genome, Viral, HLA-A Antigens (genetics), HLA-A Antigens (metabolism), HLA-A2 Antigen, HLA-B Antigens (genetics), HLA-B Antigens (metabolism), HLA-B27 Antigen, Hepatitis B Antigens (genetics), Hepatitis B virus (genetics), Hepatitis B virus (immunology), Humans, Immunodominant Epitopes (genetics), In Vitro Techniques, Protein Binding, Software, T-Lymphocytes (immunology), Thermodynamics, antigen, epitope prediction, free energy scoring, homology modelling, major histocompatibility complex, threading.
- MESH :
- chemical , genetics : HLA-A Antigens, HLA-B Antigens, Hepatitis B Antigens, Immunodominant Epitopes.
- chemical , metabolism : HLA-A Antigens, HLA-B Antigens.
- genetics : Hepatitis B virus.
- immunology : Hepatitis B virus, T-Lymphocytes.
- statistics & numerical data : Epitope Mapping.
- Alleles, Amino Acid Motifs, Amino Acid Sequence, Databases, Genetic, Genome, Viral, HLA-A2 Antigen, HLA-B27 Antigen, Humans, In Vitro Techniques, Protein Binding, Software, Thermodynamics.
Abstract
Abstract: A new computational method (EpiDock) is proposed for predicting peptide binding to class I MHC proteins, from the amino acid sequence of any protein of immunological interest. Starting from the primary structure of the target protein, individual three-dimensional structures of all possible MHC-peptide (8-, 9- and 10-mers) complexes are obtained by homology modelling. A free energy scoring function (Fresno) is then used to predict the absolute binding free energy of all possible peptides to the class I MHC restriction protein. Assuming that immunodominant epitopes are usually found among the top MHC binders, the method can thus be applied to predict the location of immunogenic peptides on the sequence of the protein target. When applied to the prediction of HLA-A*0201-restricted T-cell epitopes from the Hepatitis B virus, EpiDock was able to recover 92% of known high affinity binders and 80% of known epitopes within a filtered subset of all possible nonapeptides corresponding to about one tenth of the full theoretical list. The proposed method is fully automated and fast enough to scan a viral genome in less than an hour on a parallel computing architecture. As it requires very few starting experimental data, EpiDock can be used: (i) to predict potential T-cell epitopes from viral genomes (ii) to roughly predict still unknown peptide binding motifs for novel class I MHC alleles.
Url:
DOI: 10.1023/A:1020244329512
Affiliations:
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Le document en format XML
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<term>Epitope Mapping (statistics & numerical data)</term>
<term>Genome, Viral</term>
<term>HLA-A Antigens (genetics)</term>
<term>HLA-A Antigens (metabolism)</term>
<term>HLA-A2 Antigen</term>
<term>HLA-B Antigens (genetics)</term>
<term>HLA-B Antigens (metabolism)</term>
<term>HLA-B27 Antigen</term>
<term>Hepatitis B Antigens (genetics)</term>
<term>Hepatitis B virus (genetics)</term>
<term>Hepatitis B virus (immunology)</term>
<term>Humans</term>
<term>Immunodominant Epitopes (genetics)</term>
<term>In Vitro Techniques</term>
<term>Protein Binding</term>
<term>Software</term>
<term>T-Lymphocytes (immunology)</term>
<term>Thermodynamics</term>
<term>antigen</term>
<term>epitope prediction</term>
<term>free energy scoring</term>
<term>homology modelling</term>
<term>major histocompatibility complex</term>
<term>threading</term>
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<term>Antigènes HLA-A (génétique)</term>
<term>Antigènes HLA-A (métabolisme)</term>
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<term>Bases de données génétiques</term>
<term>Cartographie épitopique ()</term>
<term>Génome viral</term>
<term>Humains</term>
<term>Liaison aux protéines</term>
<term>Logiciel</term>
<term>Lymphocytes T (immunologie)</term>
<term>Motifs d'acides aminés</term>
<term>Séquence d'acides aminés</term>
<term>Techniques in vitro</term>
<term>Thermodynamique</term>
<term>Virus de l'hépatite B (génétique)</term>
<term>Virus de l'hépatite B (immunologie)</term>
<term>Épitopes immunodominants (génétique)</term>
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<term>Hepatitis B Antigens</term>
<term>Immunodominant Epitopes</term>
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<term>Motifs d'acides aminés</term>
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<front><div type="abstract" xml:lang="en">Abstract: A new computational method (EpiDock) is proposed for predicting peptide binding to class I MHC proteins, from the amino acid sequence of any protein of immunological interest. Starting from the primary structure of the target protein, individual three-dimensional structures of all possible MHC-peptide (8-, 9- and 10-mers) complexes are obtained by homology modelling. A free energy scoring function (Fresno) is then used to predict the absolute binding free energy of all possible peptides to the class I MHC restriction protein. Assuming that immunodominant epitopes are usually found among the top MHC binders, the method can thus be applied to predict the location of immunogenic peptides on the sequence of the protein target. When applied to the prediction of HLA-A*0201-restricted T-cell epitopes from the Hepatitis B virus, EpiDock was able to recover 92% of known high affinity binders and 80% of known epitopes within a filtered subset of all possible nonapeptides corresponding to about one tenth of the full theoretical list. The proposed method is fully automated and fast enough to scan a viral genome in less than an hour on a parallel computing architecture. As it requires very few starting experimental data, EpiDock can be used: (i) to predict potential T-cell epitopes from viral genomes (ii) to roughly predict still unknown peptide binding motifs for novel class I MHC alleles.</div>
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