Recovery of known T-cell epitopes by computational scanning of a viral genome
Identifieur interne : 000164 ( Istex/Corpus ); précédent : 000163; suivant : 000165Recovery of known T-cell epitopes by computational scanning of a viral genome
Auteurs : Antoine Logean ; Didier RognanSource :
- Journal of Computer-Aided Molecular Design [ 0920-654X ] ; 2002-04-01.
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- KwdEn :
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.
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DOI: 10.1023/A:1020244329512
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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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<Para>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<Superscript>*</Superscript>
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.</Para>
<Para>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.</Para>
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<place><placeTerm type="text">Dordrecht</placeTerm>
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<dateIssued encoding="w3cdtf">2002-04-01</dateIssued>
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<abstract 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.</abstract>
<subject lang="en"><topic>antigen</topic>
<topic>epitope prediction</topic>
<topic>free energy scoring</topic>
<topic>homology modelling</topic>
<topic>major histocompatibility complex</topic>
<topic>threading</topic>
</subject>
<relatedItem type="host"><titleInfo><title>Journal of Computer-Aided Molecular Design</title>
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<titleInfo type="abbreviated"><title>J Comput Aided Mol Des</title>
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<originInfo><publisher>Springer</publisher>
<dateIssued encoding="w3cdtf">2002-04-01</dateIssued>
<copyrightDate encoding="w3cdtf">2002</copyrightDate>
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<subject><genre>Chemistry</genre>
<topic>Computer Applications in Chemistry</topic>
<topic>Physical Chemistry</topic>
<topic>Animal Anatomy / Morphology / Histology</topic>
</subject>
<identifier type="ISSN">0920-654X</identifier>
<identifier type="eISSN">1573-4951</identifier>
<identifier type="JournalID">10822</identifier>
<identifier type="IssueArticleCount">5</identifier>
<identifier type="VolumeIssueCount">12</identifier>
<part><date>2002</date>
<detail type="volume"><number>16</number>
<caption>vol.</caption>
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<detail type="issue"><number>4</number>
<caption>no.</caption>
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<extent unit="pages"><start>229</start>
<end>243</end>
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<recordInfo><recordOrigin>Kluwer Academic Publishers, 2002</recordOrigin>
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<identifier type="ark">ark:/67375/VQC-K4D7GJ9P-V</identifier>
<identifier type="DOI">10.1023/A:1020244329512</identifier>
<identifier type="ArticleID">5094325</identifier>
<identifier type="ArticleID">Art1</identifier>
<accessCondition type="use and reproduction" contentType="copyright">Kluwer Academic Publishers, 2002</accessCondition>
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