Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding |
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Authors: | Brusic V Bucci K Schönbach C Petrovsky N Zeleznikow J Kazura J W |
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Affiliation: | BIC-KRDL, Kent Ridge Digital Labs, 21 Heng Mui Keng Terrace, Singapore 119613. vladimir@krdl.org.sg |
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Abstract: | 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-A11 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. |
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