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Online Journal of Bioinformatics ©

Volume 18(2):68-77, 2017.

In silico prediction for Staphylococcus aureus vaccine epitopes.


Garima Bajetha, Sarika and Anil Rai


Center of Agricultural Bioinformatics, Indian Agriculture Statistics Research Institute (I.C.A.R) Library Avenue, Pusa Campus New Delhi-110012 INDIA




Garima Bajetha S, Anil Rai., In silico prediction for Staphylococcus aureus vaccine epitopes. Onl J Bioinform., 18(2):68-77, 2017. Staphylococcus aureus adheres to extracellular matrix and plasma proteins on orthopaedic-devices causing mastitis. To develop a peptide vaccine against the pathogen, a method to extract adhesins from the cell wall of S. aureus cell is desirable. We use In silico structure, QSAR, and simulation with fold level analysis to predict potential antigenic B-cell epitope derived T-cell epitopes from four vaccine targets. The models showed accuracy of 70-97%. VaxiJen analysis of exo-membrane full length proteins exhibited antigenicity ranging 0.4189 to 0.8080. Of 4 essential membrane proteins, staphylocoagulasecoa” showed highest antigenicity. Based on antigenicity, cluster scores, IC50 value, homology and stimulation results “TTHADGTAT” from Staphylocoagulasecoa” was most suitable for vaccine. Selected epitopes demonstrate potential to induce B-cell and T-cell mediated immune responses.


Key words: - Staphylococcus aureus, Epitope, Mastitis, vaccine designing, Immune response.