VaxiJen: a server for prediction of protective antigens,tumour antigens and subunit vaccines |
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Authors: | Irini A Doytchinova Darren R Flower |
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Institution: | (1) Faculty of Pharmacy, Medical University of Sofia, 2 Dunav St, 1000, Sofia, Bulgaria;(2) The Jenner Institute, Oxford University, RG20 7NN Compton, Berkshire, UK |
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Abstract: | Background Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative
microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success
remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This
is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures
and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to
direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity
to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free
approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform
vectors of principal amino acid properties. |
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