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Flower, Darren R; Macdonald, Isabel K.; Ramakrishnan, Kamna; Davies, Matthew N. and Doytchinova, Irini A. (2010). Computer aided selection of candidate vaccine antigens. Immunome research, 6 (Suppl.2), S1-S16.

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Abstract

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

Item Type:Article
Additional Information:© 2010 Flower et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords:Immunology, Molecular Biology, Computational Theory and Mathematics, Applied Mathematics, Computer Science Applications
Divisions:Schools_of_Study > Life & Health Sciences > Pharmacy (LHS)
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ID Code:16070
Deposited By:Prof Alfred Admin
Deposited On:22 Mar 2012 12:06
Last Modified:19 Dec 2014 08:10

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