Predicting peptides bound to I-Ag7 class II histocompatibility molecules using a novel expectation-maximization alignment algorithm.
Proteomics
; 7(3): 367-77, 2007 Feb.
Article
em En
| MEDLINE
| ID: mdl-17211830
ABSTRACT
The useful structural features of class II MHC molecules are rarely integrated into T-cell epitope predictions. We propose an approach that applies a novel expectation-maximization algorithm to align the naturally processed peptides selected by the class II MHC I-A(g7) molecule - focusing on the five MHC-specific anchor positions. Based on the alignment profile, log of odds (LOD) scores supplemented with the Laplace plus-one pseudocounts method are applied to identify the potential T-cell epitopes. In addition, an innovative computational concept of hindering residues using statistical and structural information is developed to refine the prediction. Performance analysis by receiver operating characteristics statistics and the experimental validation of the LOD scores demonstrate the accuracy of our predictive model. Furthermore, our model successfully predicts T-cell epitopes of hen egg-white lysozyme protein antigen. Our study provides a framework for predicting T-cell epitopes in class II MHC molecules.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Peptídeos
/
Algoritmos
/
Antígenos de Histocompatibilidade Classe II
/
Alinhamento de Sequência
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
Proteomics
Assunto da revista:
BIOQUIMICA
Ano de publicação:
2007
Tipo de documento:
Article
País de afiliação:
Estados Unidos