A multi-stage and multi-epitope vaccine against Mycobacterium tuberculosis based on an immunoinformatics approach / 细胞与分子免疫学杂志

Objectives To develop a multi-stage and multi-epitope vaccine, which consists of epitopes from the early secretory and latency-associated antigens of Mycobacterium tuberculosis (MTB). Methods The B-cell, cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes of 12 proteins were predicted using an immunoinformatics. The epitopes with antigenicity, without cytotoxicity and sensitization, were further screened to construct the multi-epitope vaccine. Furthermore, the proposed vaccine underwent physicochemical properties analysis and secondary structure prediction as well as 3D structure modeling, refinement and validation. Then the refined model was docked with TLR4. Finally, an immune simulation of the vaccine was carried out. Results The proposed vaccine, which consists of 12 B-cell, 11 CTL and 12 HTL epitopes, had a flexible and stable globular conformation as well as a thermostable and hydrophilic structure. A stable interaction of the vaccine with TLR4 was confirmed by molecular docking. The efficiency of the candidate vaccine to trigger effective cellular and humoral immune responses was assessed by immune simulation. Conclusion A multi-stage multi-epitope MTB vaccine construction strategy based on immunoinformatics is proposed, which is expected to prevent both active and latent MTB infection.

Homology modeling and epitope prediction of Der f 33

Dermatophagoides farinae (Der f), one of the main species of house dust mites, produces more than 30 allergens. A recently identified allergen belonging to the alpha-tubulin protein family, Der f 33, has not been characterized in detail. In this study, we used bioinformatics tools to construct the secondary and tertiary structures and predict the B and T cell epitopes of Der f 33. First, protein attribution, protein patterns, and physicochemical properties were predicted. Then, a reasonable tertiary structure was constructed by homology modeling. In addition, six B cell epitopes (amino acid positions 34-45, 63-67, 103-108, 224-230, 308-316, and 365-377) and four T cell epitopes (positions 178-186, 241-249, 335-343, and 402-410) were predicted. These results established a theoretical basis for further studies and eventual epitope-based vaccine design against Der f 33.

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.