Prediction of binding to MHC class I molecules
Identifieur interne : 004071 ( Main/Exploration ); précédent : 004070; suivant : 004072Prediction of binding to MHC class I molecules
Auteurs : Hans-Peter Adams [États-Unis] ; James A. Koziol [États-Unis]Source :
- Journal of Immunological Methods [ 0022-1759 ] ; 1995.
English descriptors
- Teeft :
- Activation function, Amino, Amino acid sequence, Amino acid sequences, Binding capacities, Binding capacity, Classification role, Classification rule, Classification scheme, Classification schemes, Critical reading, Decamer peptides, Decamers, Immune response, Input patterns, Inversion, Inversion method, Kast, Large database, Linear separation, Lymphocyte epitopes, Molecule, Neural, Neural nets, Neural networks, Nonamers, Other groups, Output function, Peptide, Peptide sequences, Prospective evaluation, Random values.
Abstract
Abstract: The binding of antigenic peptide sequences to major histocompatibility complex (MHC) molecules is a prerequisite for stimulation of cytotoxic T cell responses. Neural networks are here used to predict the binding capacity of polypeptides to MHC class I molecules encoded by the gene HLA-A ∗ 0201. Given a large database of 552 nonamers and 486 decamers and their known binding capacities, the neural networks achieve a predictive hit rate of 0.78 for classifying peptides which might induce an immune response (good or intermediate binders) vs. those which cannot (weak or non-binders). The neural nets also depict specific motifs for different binding capacities. This approach is in principle applicable to all MHC class I and II molecules, given a suitable set of known binding capacities. The trained networks can then be used to perform a systematic search through all pathogen or tumor antigen protein sequences for potential cytotoxic T lymphocyte epitopes.
Url:
DOI: 10.1016/0022-1759(95)00111-M
Affiliations:
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Torrey Pines Road. La Jolla, CA 92037</wicri:regionArea><wicri:noRegion>CA 92037</wicri:noRegion></affiliation></author><author><name sortKey="Koziol, James A" sort="Koziol, James A" uniqKey="Koziol J" first="James A." last="Koziol">James A. Koziol</name><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of Biomathematics, Mail Drop SBR-7, Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10666 N. Torrey Pines Road. La Jolla, CA 92037</wicri:regionArea><wicri:noRegion>CA 92037</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="j">Journal of Immunological Methods</title><title level="j" type="abbrev">JIM</title><idno type="ISSN">0022-1759</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1995">1995</date><biblScope unit="volume">185</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="page" from="181">181</biblScope><biblScope unit="page" to="190">190</biblScope></imprint><idno type="ISSN">0022-1759</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0022-1759</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="Teeft" xml:lang="en"><term>Activation function</term><term>Amino</term><term>Amino acid sequence</term><term>Amino acid sequences</term><term>Binding capacities</term><term>Binding capacity</term><term>Classification role</term><term>Classification rule</term><term>Classification scheme</term><term>Classification schemes</term><term>Critical reading</term><term>Decamer peptides</term><term>Decamers</term><term>Immune response</term><term>Input patterns</term><term>Inversion</term><term>Inversion method</term><term>Kast</term><term>Large database</term><term>Linear separation</term><term>Lymphocyte epitopes</term><term>Molecule</term><term>Neural</term><term>Neural nets</term><term>Neural networks</term><term>Nonamers</term><term>Other groups</term><term>Output function</term><term>Peptide</term><term>Peptide sequences</term><term>Prospective evaluation</term><term>Random values</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: The binding of antigenic peptide sequences to major histocompatibility complex (MHC) molecules is a prerequisite for stimulation of cytotoxic T cell responses. Neural networks are here used to predict the binding capacity of polypeptides to MHC class I molecules encoded by the gene HLA-A ∗ 0201. Given a large database of 552 nonamers and 486 decamers and their known binding capacities, the neural networks achieve a predictive hit rate of 0.78 for classifying peptides which might induce an immune response (good or intermediate binders) vs. those which cannot (weak or non-binders). The neural nets also depict specific motifs for different binding capacities. This approach is in principle applicable to all MHC class I and II molecules, given a suitable set of known binding capacities. The trained networks can then be used to perform a systematic search through all pathogen or tumor antigen protein sequences for potential cytotoxic T lymphocyte epitopes.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country></list><tree><country name="États-Unis"><noRegion><name sortKey="Adams, Hans Peter" sort="Adams, Hans Peter" uniqKey="Adams H" first="Hans-Peter" last="Adams">Hans-Peter Adams</name></noRegion><name sortKey="Koziol, James A" sort="Koziol, James A" uniqKey="Koziol J" first="James A." last="Koziol">James A. Koziol</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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