Recovery of known T-cell epitopes by computational scanning of a viral genome.
Identifieur interne : 002485 ( PubMed/Corpus ); précédent : 002484; suivant : 002486Recovery 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.
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.
- 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
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.
DOI: 10.1023/a:1020244329512
PubMed: 12400854
Links to Exploration step
pubmed:12400854Le document en format XML
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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></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">12400854</PMID><DateCompleted><Year>2003</Year><Month>04</Month><Day>10</Day></DateCompleted><DateRevised><Year>2019</Year><Month>11</Month><Day>06</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0920-654X</ISSN><JournalIssue CitedMedium="Print"><Volume>16</Volume><Issue>4</Issue><PubDate><Year>2002</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Journal of computer-aided molecular design</Title><ISOAbbreviation>J. Comput. Aided Mol. Des.</ISOAbbreviation></Journal><ArticleTitle>Recovery of known T-cell epitopes by computational scanning of a viral genome.</ArticleTitle><Pagination><MedlinePgn>229-43</MedlinePgn></Pagination><Abstract><AbstractText>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.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Logean</LastName><ForeName>Antoine</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Bioinformatic Group, Laboratoire de Pharmacochimie de la Communication Cellulaire, UMR CNRS 7081, Illkirch, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rognan</LastName><ForeName>Didier</ForeName><Initials>D</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>J Comput Aided Mol 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