Design of MHC I stabilizing peptides by agent-based exploration of sequence space.
Protein Eng Des Sel
; 20(3): 99-108, 2007 Mar.
Article
em En
| MEDLINE
| ID: mdl-17314106
ABSTRACT
Identification of molecular features that determine peptide interaction with major histocompatibility complex I (MHC I) is essential for vaccine development. We have developed a concept for peptide design by combining an agent-based artificial ant system with artificial neural networks. A jury of feedforward networks classifies octapeptides that are recognized by mouse MHC I protein H-2K(b). Prediction accuracy yielded a correlation coefficient of 0.94. Peptides were designed in machina by the artificial ant system and tested in vitro for their MHC I stabilizing effect. The behavior of the search agents during the design process was controlled by the jury network. The experimentally determined prediction accuracy was 89% for the designed stabilizing and 95% for the non-stabilizing peptides. Novel H-2K(b) stabilizing peptides were conceived that reveal extensions of known residue motifs. The combined network-agent system recognized context dependencies of residue positions. A diverse set of novel sequences exhibiting substantial activity was generated.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Peptídeos
/
Antígenos de Histocompatibilidade Classe I
/
Engenharia de Proteínas
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Protein Eng Des Sel
Assunto da revista:
BIOQUIMICA
/
BIOTECNOLOGIA
Ano de publicação:
2007
Tipo de documento:
Article
País de afiliação:
Alemanha