Application of an artificial neural network to predict specific class I MHC binding peptide sequences.
Nat Biotechnol
; 16(8): 753-6, 1998 Aug.
Article
en En
| MEDLINE
| ID: mdl-9702774
Computational methods were used to predict the sequences of peptides that bind to the MHC class I molecule, K(b). The rules for predicting binding sequences, which are limited, are based on preferences for certain amino acids in certain positions of the peptide. It is apparent though, that binding can be influenced by the amino acids in all of the positions of the peptide. An artificial neural network (ANN) has the ability to simultaneously analyze the influence of all of the amino acids of the peptide and thus may improve binding predictions. ANNs were compared to statistically analyzed peptides for their abilities to predict the sequences of K(b) binding peptides. ANN systems were trained on a library of binding and nonbinding peptide sequences from a phage display library. Statistical and ANN methods identified strong binding peptides with preferred amino acids. ANNs detected more subtle binding preferences, enabling them to predict medium binding peptides. The ability to predict class I MHC molecule binding peptides is useful for immunolological therapies involving cytotoxic-T cells.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Oligopéptidos
/
Antígenos H-2
/
Redes Neurales de la Computación
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
Idioma:
En
Revista:
Nat Biotechnol
Asunto de la revista:
BIOTECNOLOGIA
Año:
1998
Tipo del documento:
Article
País de afiliación:
Estados Unidos