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

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Brusic, Vladimir, Bucci, Kim, Schonbach, Christian, Petrovsky, Nikolai, Zeleznikow, John ORCID: 0000-0002-8786-2644 and Kazura, James W (2001) Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding. Journal of Molecular Graphics and Modelling, 19 (5). pp. 405-411. ISSN 1093-3263


Laboratory experimentation in immunology, particularly that related to antigen recognition and determination of specific targets of immune response, has become a combinatorial challenge. Understanding the mechanisms of immune recognition and specificity and selection of immune response targets is necessary before this information can be applied systematically to the design of vaccines and immunotherapeutics. We anticipate that dynamic models of immune interactions that can absorb the ever-increasing amount of data generated in the field and self-improve with the accumulation of data and knowledge will become standard methodology in immunology research. Standardization and exploitation of the synergies of modelling and experimental methods provide an efficient means for largescale epitope screening. This study provides a first-level guideline for cyclical refinement of computer models and their integration with laboratory experiments.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/10668
DOI 10.1016/S1093-3263(00)00099-1
Official URL http://www.sciencedirect.com/science/article/pii/S...
Subjects Historical > RFCD Classification > 280000 Information, Computing and Communication Sciences
Historical > FOR Classification > 0601 Biochemistry and Cell Biology
Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 1107 Immunology
Historical > Faculty/School/Research Centre/Department > School of Management and Information Systems
Keywords ResPubID12223, T-cell epitopes, malaria vaccine, T lymphocytes, major histocompatibility complex, MHC molecules, artificial neural networks, ANNs, hidden Markov models, HMMs
Citations in Scopus 36 - View on Scopus
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