A najor current challenge and constraint in cervical cancer research is the development of vaccines againsthuman papilloma virus (HPV) epitopes. Although many studies are done on epitope identification on HPVs,no computational work has been carried out for high risk forms which are considered to cause cervical cancer.Of all the high risk HPVs, HPV 16, HPV 18 and HPV 45 are responsible for 94% of cervical cancers in womenworldwide. In this work, we computationally predicted the promiscuous epitopes among the E6 proteins ofhigh risk HPVs. We identified the conserved residues, HLA class I, HLA class II and B-cell epitopes along withtheir corresponding secondary structure conformations. We used extremely precise bioinformatics tools likeClustalW2, MAPPP, NetMHC, EpiJen, EpiTop 1.0, ABCpred, BCpred and PSIPred for achieving this task.Our study identified specific regions ‘FAFR(K)DL’ followed by ‘KLPD(Q)LCTEL’ fragments which proved tobe promiscuous epitopes present in both human leukocyte antigen (HLA) class I, class II molecules and B cellsas well. These fragments also follow every suitable character to be considered as promiscuous epitopes withsupporting evidences of previously reported experimental results. Thus, we conclude that these regions shouldbe considered as the important for design of specific therapeutic vaccines for cervical cancer.
(2013). Prediction of Promiscuous Epitopes in the E6 Protein of Three High Risk Human Papilloma Viruses: A Computational Approach. Asian Pacific Journal of Cancer Prevention, 14(7), 4167-4175.
MLA
. "Prediction of Promiscuous Epitopes in the E6 Protein of Three High Risk Human Papilloma Viruses: A Computational Approach". Asian Pacific Journal of Cancer Prevention, 14, 7, 2013, 4167-4175.
HARVARD
(2013). 'Prediction of Promiscuous Epitopes in the E6 Protein of Three High Risk Human Papilloma Viruses: A Computational Approach', Asian Pacific Journal of Cancer Prevention, 14(7), pp. 4167-4175.
VANCOUVER
Prediction of Promiscuous Epitopes in the E6 Protein of Three High Risk Human Papilloma Viruses: A Computational Approach. Asian Pacific Journal of Cancer Prevention, 2013; 14(7): 4167-4175.