Computational prediction of nuclear localization signals and structural characteristics of 91 types of HPV E6 proteins.

Human papillomaviruses (HPVs) are small DNA tumor viruses that replicate and assemble exclusively in the nucleus. Thus their proteins, including E6, must carry nuclear localization signals (NLSs) to enter the nucleus. To analyze and to predict the nuclear localization signals and several post translational modifications by bioinformatics analysis, we obtained 91 E6 protein sequences from available databases. To investigate the localization of these sequences, we used Hum-Ploc software. Homology and alignment of sequences were performed by Blast software and Multalin server respectively. Prediction of N-glycosylation and serine, threonine and tyrosine phosphorylation sites of HPV E6 protein sequences was accomplished with NetNGlyc and NetPhos software. Out of 91 types, the NLSs of 29 types were predicted by signal-3L and signal-CF software. We tried to predict the NLSs of remaining HPV E6 proteins according to the homology of the already predicted NLSs. However, because of considerable variation between E6 protein sequences, we could not classify the NLSs in monopartite or bipartite. According to the results, all NLSs of HPV E6 proteins could be assigned to 11 categories. NLSs of several HPV E6 protein sequences were also determined by experimental studies. Overall, different types of HPV E6 protein in same category show approximately similar pattern in post translational modifications such as N-glycosylation and phosphorylation. Some HPV early genes, such as E6, are known to act as oncogenes that promote tumor growth and malignant transformation. Thus more detailed recognition of nuclear localizing sequences and nucleocytoplasmic transport pathway can play a key role in prevention and treatment of HPV infection and related cancers. The results also show that bioinformatics technology can direct and simplify experimental studies.

Prediction of epitopes and structural properties of Iranian HPV-16 E6 by bioinformatics methods.

HPV-16 is the HPV most often linked to cervical carcinoma. E6 of the HPV-16 which expressed early in cancer cells is a target for immune therapeutic methods. In the present study, after fetching the sequence of HPV-16 E6 (accession No: ABC48950) from NCBI databank, by using hydrophilicity, flexibility, accessibility, turns, exposed surface, polarity and antigenic propensity scales, B cell epitopes of the protein were predicted. In addition, MHCPred version 2.0 program was used to predict MHC Class I and Class II alleles. The sequences of the epitopes were also found out. According to this computer-based prediction the results from A0203 and DRB0101 reveal lower IC50 than other alleles. For A0203 allele, peptide with the best binding affinity was 25ELQTTIHDI33. For DRB0101 allele, the peptide was 39YCKQQLLRR47. Different structural features of the protein were also predicted. These features were including glycosylation, kinase C phosphorylation, Casein kinase II phosphorylation and N-myristylation sites, and disulfide bonding states. By using these computational scales and programs, 0 glycosylation, 3 kinase C phosphorylation, 2 casein kinase II phosphorylation and 1 N-myristylation sites and 2 disulfide bonds were predicted. Development and approval of new vaccines are keys for control of cancer. Epitopes and structural features of proteins can be predicted and this information can help us in molecular and medical studies of viruses.