Discriminating antigen and non-antigen using proteome dissimilarity III: tumour and parasite antigens



Kamna Ramakrishnan1, Darren R. Flower2,*


1The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, United Kingdom; RG20 7NN Medical Genetics Section, University of Edinburgh, Edinburgh, United Kingdom EH4 2XU; 2Aston University, Life and Health Sciences, Aston University, Aston Triangle, Birmingham, United Kingdom, B5 7ET


Email;* Corresponding author


Article Type




received May 27, 2010; accepted June 09, 2010, published June 24, 2010



Computational genome analysis enables systematic identification of potential immunogenic proteins within a pathogen. Immunogenicity is a system property that arises through the interaction of host and pathogen as mediated through the medium of a immunogenic protein. The overt dissimilarity of pathogenic proteins when compared to the host proteome is conjectured by some to be the determining principal of immunogenicity. Previously, we explored this idea in the context of Bacterial, Viral, and Fungal antigen. In this paper, we broaden and extend our analysis to include complex antigens of eukaryotic origin, arising from tumours and from parasite pathogens. For both types of antigen, known antigenic and non-antigenic protein sequences were compared to human and mouse proteomes. In contrast to our previous results, both visual inspection and statistical evaluation indicate a much wider range of homologues and a significant level of discrimination; but, as before, we could not determine a viable threshold capable of properly separating non-antigen from antigen. In concert with our previous work, we conclude that global proteome dissimilarity is not a useful metric for immunogenicity for presently available antigens arising from Bacteria, viruses, fungi, parasites, and tumours. While we see some signal for certain antigen types, using dissimilarity is not a useful approach to identifying antigenic molecules within pathogen genomes.



Ramakrishnan et al. Bioinformation 5(1):39-42 (2010)

Edited by

P. Kangueane






Biomedical Informatics



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