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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
2
|
| pubmed:dateCreated |
1995-11-1
|
| pubmed:abstractText |
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.
|
| pubmed:grant | |
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:chemical | |
| pubmed:status |
MEDLINE
|
| pubmed:month |
Sep
|
| pubmed:issn |
0022-1759
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:day |
25
|
| pubmed:volume |
185
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
181-90
|
| pubmed:dateRevised |
2007-11-14
|
| pubmed:meshHeading | |
| pubmed:year |
1995
|
| pubmed:articleTitle |
Prediction of binding to MHC class I molecules.
|
| pubmed:affiliation |
Department of Molecular and Experimental Medicine, Scripps Research Institute, La Jolla, CA 92037, USA.
|
| pubmed:publicationType |
Journal Article,
In Vitro,
Research Support, U.S. Gov't, P.H.S.
|