Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding


Autoria(s): Brusic, V; Bucci, K; Schonbach, C; Petrovsky, N; Zeleznikow, J; Kazura, JW
Data(s)

01/01/2001

Resumo

Peptides that induce and recall T-cell responses are called T-cell epitopes. T-cell epitopes may be useful in a subunit vaccine against malaria. Computer models that simulate peptide binding to MHC are useful for selecting candidate T-cell epitopes since they minimize the number of experiments required for their identification. We applied a combination of computational and immunological strategies to select candidate T-cell epitopes. A total of 86 experimental binding assays were performed in three rounds of identification of HLA-All binding peptides from the six preerythrocytic malaria antigens. Thirty-six peptides were experimentally confirmed as binders. We show that the cyclical refinement of the ANN models results in a significant improvement of the efficiency of identifying potential T-cell epitopes. (C) 2001 by Elsevier Science Inc.

Identificador

http://espace.library.uq.edu.au/view/UQ:37536

Idioma(s)

eng

Palavras-Chave #Biochemical Research Methods #Biochemistry & Molecular Biology #Computer Science, Interdisciplinary Applications #Crystallography #Cytotoxic T-lymphocytes #Mhc Class-i #Neural-network #Cell Epitopes #Hla-a #Molecules #Prediction #Antigen #Motifs #Identification
Tipo

Journal Article