11) Large-scale analysis of antigenic diversity of T-cell epitopes in dengue virus.
Khan AM, Heiny A, Lee KX, Srinivasan K, Tan TW, August JT, Brusic V.
BMC Bioinformatics. 2006 Dec 18;7 Suppl 5:S4.
PUBMED PMID: 17254309
Out link: Full-text
Impact Factor Year 2009: 3.428
No. of Citations: 16 (total): 8 (non-self) & 8 (self)
ABSTRACT :
BACKGROUND : Antigenic diversity in dengue virus strains has been studied, but large-scale and detailed systematic analyses have not been reported. In this study, we report a bioinformatics method for analyzing viral antigenic diversity in the context of T-cell mediated immune responses. We applied this method to study the relationship between short-peptide antigenic diversity and protein sequence diversity of dengue virus. We also studied the effects of sequence determinants on viral antigenic diversity. Short peptides, principally 9-mers were studied because they represent the predominant length of binding cores of T-cell epitopes, which are important for formulation of vaccines.
RESULTS : Our analysis showed that the number of unique protein sequences required to represent complete antigenic diversity of short peptides in dengue virus is significantly smaller than that required to represent complete protein sequence diversity. Short-peptide antigenic diversity shows an asymptotic relationship to the number of unique protein sequences, indicating that for large sequence sets (~200) the addition of new protein sequences has marginal effect to increasing antigenic diversity. A near-linear relationship was observed between the extent of antigenic diversity and the length of protein sequences, suggesting that, for the practical purpose of vaccine development, antigenic diversity of short peptides from dengue virus can be represented by short regions of sequences (~<100>
CONCLUSION : This study provides evidence that there are limited numbers of antigenic combinations in protein sequence variants of a viral species and that short regions of the viral protein are sufficient to capture antigenic diversity of T-cell epitopes. The approach described herein has direct application to the analysis of other viruses, in particular those that show high diversity and/or rapid evolution, such as influenza A virus and human immunodeficiency virus (HIV).
This article has been cited by other articles/sites:
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2) Khan AM, Miotto O, Heiny AT, Salmon J, Srinivasan KN, Nascimento EJ, Marques ET Jr, Brusic V, Tan TW, August JT. A systematic bioinformatics approach for selection of epitope-based vaccine targets. Cell Immunol. 2006 Dec;244(2):141-7. Epub 2007 Apr 16. PMID: 17434154
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8) Zhang, GL, Khan, AM, Srinivasan, KN, Heiny, AT, Lee, KX, Kwoh, CK, August JT, Brusic, V. Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes. BMC Bioinformatics 2008, 9(Suppl 1):S19
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10) Tongchusak S, Leelayuwat C, Brusic V, Chaiyaroj SC. In silico prediction and immunological validation of common HLA-DRB1-restricted T cell epitopes of Candida albicans secretory aspartyl proteinase 2. Microbiol Immunol. 2008 Apr;52(4):231-42. PMID: 18426398
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