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Predicted epitopes of H5N1 bird flu virus by bioinformatics method: a clue for further vaccine development

2006; Lippincott Williams & Wilkins; Volume: 119; Issue: 20 Linguagem: Inglês

10.1097/00029330-200610020-00017

2542-5641

Viroj Wiwanitkit,

Influenza Virus Research Studies

To the Editor: Bird flu or avian flu, caused by H5N1 virus, is a new emerging infectious disease. It is noted that this H5N1 virus jumped the species barrier and caused severe disease with high mortality in humans in many countries. The continued westward dissemination of H5N1 influenza A viruses in avian populations and the nearly 50% mortality of humans infected with H5N1 are a source of great international concern.1 Providing sufficient antiviral drugs and development and approval of new vaccines are the keys for control of the possible emerging pandemic of this atypical influenza.1,2 Based on the advance in bioinformatics, the immunomics becomes a new alternative in vaccine development.3 Advanced technologies for vaccine development, such as genome sequence analysis, microarray, proteomics approach, high-throughput cloning, bioinformatics database tools and computational vaccinology can be applied for vaccine development of several diseases including new emerging diseases. Prediction of peptide binding to major histocompatibility complex (MHC) molecules is a basis for epitope discovery-driven vaccine development. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Databases and data mining are two principal weapons at the disposal of the in silico vaccinologist. Faced with the expanding volume of information now available from genome databases, vaccinologists are turning to epitope mapping tools to screen vaccine candidates.4 New databases have been launched in order to facilitate the epitope prediction. For influenza, HLA-A*0101, HLA-B*2705, HLA-B*3501 alleles are preferentially used in the influenza virus-specific T lymphocyte response.5 Here, the author reports the preliminary data from the computation analysis of available H5N1 genome to find potential T-cell epitopes using bioinformatics tool namely MHCPred.6 The MHCPred is a partial least squares-based multivariate robust statistical approach to the quantitative prediction of peptide binding to MHC.6 The analysis shows that amino acid sequence "718 TATTGCAT 726 (Predicted IC50 value = 89.54 nmol/L)" poses the highest epitope property (IC50 value) followed by "G 347 TACAGTCC 355 (Predicted IC50 value = 129.44 nmol/L)" and "G 1457 TATTGACC 1465 (Predicted IC50 value = 133.05 nmol/L)". These data are useful for further vaccine development because these promiscuous peptide binders allow to minimize the total number of predicted epitopes without compromising the population coverage required in the design of multi-epitope vaccines.