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

Brusic, V, Bucci, K, Schonbach, C, Petrovsky, N, Zeleznikow, J and Kazura, JW (2001) Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding. Journal of Molecular Graphics & Modelling, 19 5: 405-+. doi:10.1016/S1093-3263(00)00099-1


Author Brusic, V
Bucci, K
Schonbach, C
Petrovsky, N
Zeleznikow, J
Kazura, JW
Title Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding
Journal name Journal of Molecular Graphics & Modelling   Check publisher's open access policy
ISSN 1093-3263
Publication date 2001-01-01
Sub-type Article (original research)
DOI 10.1016/S1093-3263(00)00099-1
Volume 19
Issue 5
Start page 405
End page +
Total pages 8
Language eng
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-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.
Keyword 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
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Agriculture and Food Sciences
 
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Created: Mon, 13 Aug 2007, 22:30:55 EST