Graphical abstract

High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46–62 and 65–76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections.

Multi-target and combinatorial therapies have been focused for the past several decades. These approaches achieved considerable therapeutic efficacy by modulating the activities of the targets in complex diseases such as HIV-1 infection, cancer and diabetes disease. Most of the diseases cannot be treated efficiently in terms of single gene target, because it involves the cessation of the coordinated function of distinct gene groups. Most of the cellular components work efficiently by interacting with other cellular components and all these interactions together represent interactome. This interconnectivity shows that a defect in a single gene may not be restricted to the gene product itself, but may spread along the network. So, drug development must be based on the network-based perspective of disease mechanisms. Many systematic diseases like neurodegenerative disorders, cancer and cardiovascular cannot be treated efficiently by the single gene target strategy because these diseases involve the complex biological machinery. In clinical trials, many mono-therapies have been found to be less effective. In mono-therapies, the long term treatment, for the systematic diseases make the diseases able to acquired resistance because of the disease nature of the natural evolution of feedback loop and pathway redundancy. Multi-target drugs might be more efficient. Multi-target therapeutics might be less vulnerable because of the inability of the biological system to resist multiple actions. In this study, we will overview the recent advances in the development of methodologies for the identification of drug target interaction and its application in the poly-pharmacology profile of the drug.

Epstein–Barr virus (EBV), also known as human herpesvirus 4 (HHV-4), is a member of the Herpesviridae family and causes infectious mononucleosis, Burkitt’s lymphoma, and nasopharyngeal carcinoma. Even in the United States of America, the situation is alarming, as EBV affects 95% of the young population between 35 and 40 years of age. In this study, both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted by using the ElliPro and NetCTL.1.2 webservers for EBV proteins (GH, GL, GB, GN, GM, GP42 and GP350). Molecular modelling tools were used to predict the 3D coordinates of peptides, and these peptides were then docked against the MHC molecules to obtain peptide-MHC complexes. Studies of their post-docking interactions helped to select potential candidates for the development of peptide vaccines. Our results predicted a total of 58 T-cell epitopes of EBV;  where the most potential were selected based on their TAP, MHC binding and C-terminal Cleavage score. The top most peptides were subjected to MD simulation and stability analysis. Validation of our predicted epitopes using a 0.45 µM concentration was carried out by using a systems biology approach. Our results suggest a panel of epitopes that could be used to immunize populations to protect against multiple diseases caused by EBV.

Cancer immunoinformatics have led to new directions towards vaccine design from predicted potential epitope candidates, which are able to stimulate the correct cellular or humoral immune responses. They were employed to accomplish an advanced vaccine design through reverse vaccinology by replacing the whole organisms. In this review, computational tools play an essential role in evaluating multiple proteomes to identify and select the potential targeted epitopes or combinations of distinct epitopes, so that candidates may afford a rationale design competent for obtaining suitable cytotoxic T lymphocytes (CTL) or B cell-mediated immune responses. This review is a complete collection of the most beneficial online and user-friendly immunological tools, servers, and databases for the design and development of the peptide vaccine. The mechanism of major histocompatability (MHC)-restricted peptide presentation and how these tools support the vaccine development are also presented. The human papillomavirus (HPV) is taken as a model microbial strain for peptide vaccine design and its sensitization against HPV-induced cervical cancer is discussed.