In silico prediction of cancer immunogens: current state of the art

Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines

Peptide antigens derived from tumors have been observed to elicit protective immune responses, categorized as either tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs). Subunit cancer vaccines incorporating these antigens have shown promise in inducing protective immune responses, leading to cancer prevention or eradication. Over recent years, peptide-based cancer vaccines have gained popularity as a treatment modality and are often combined with other forms of cancer therapy. Several clinical trials have explored the safety and efficacy of peptide-based cancer vaccines, with promising outcomes. Advancements in techniques such as whole-exome sequencing, next-generation sequencing, and in silico methods have facilitated the identification of antigens, making it increasingly feasible. Furthermore, the development of novel delivery methods and a deeper understanding of tumor immune evasion mechanisms have heightened the interest in these vaccines among researchers. This article provides an overview of novel insights regarding advancements in the field of peptide-based vaccines as a promising therapeutic avenue for cancer treatment. It summarizes existing computational methods for tumor neoantigen prediction, ongoing clinical trials involving peptide-based cancer vaccines, and recent studies on human vaccination experiments.

The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn's disease

Reciprocal interactions between host T helper cells and gut microbiota enforce local immunological tolerance and modulate extra-intestinal immunity. However, our understanding of antigen-specific tolerance to the microbiome is limited. Here, we developed a systematic approach to predict HLA class-II-specific epitopes using the humanized bacteria-originated T cell antigen (hBOTA) algorithm. We identified a diverse set of microbiome epitopes spanning all major taxa that are compatible with presentation by multiple HLA-II alleles. In particular, we uncovered an immunodominant epitope from the TonB-dependent receptor SusC that was universally recognized and ubiquitous among Bacteroidales. In healthy human subjects, SusC-reactive T cell responses were characterized by IL-10-dominant cytokine profiles, whereas in patients with active Crohn's disease, responses were associated with elevated IL-17A. Our results highlight the potential of targeted antigen discovery within the microbiome to reveal principles of tolerance and functional transitions during inflammation.

Identification and Design of a Next-Generation Multi Epitopes Bases Peptide Vaccine Candidate Against Prostate Cancer: An In Silico Approach

Prostate cancer (PCa) is the second most diagnosed cancer in men and ranked fifth in overall cancer diagnosis. During the past decades, it has arisen as a significant life-threatening disease in men at an older age. At the early onset of illness when it is in localized form, radiation and surgical treatments are applied against this disease. In case of adverse situations androgen deprivation therapy, chemotherapy, hormonal therapy, etc. are widely used as a therapeutic element. However, studies found the occurrences of several side effects after applying these therapies. In current work, several immunoinformatic techniques were applied to formulate a multi-epitopic vaccine from the overexpressed antigenic proteins of PCa. A total of 13 epitopes were identified from the five prostatic antigenic proteins (PSA, PSMA, PSCA, STEAP, and PAP), after validation with several in silico tools. These epitopes were fused to form a vaccine element by (GGGGS)3 peptide linker. Afterward, 5, 6-dimethylxanthenone-4-acetic acid (DMXAA) was used as an adjuvant to initiate and induce STING-mediated cytotoxic cascade. In addition, molecular docking was performed between the vaccine element and HLA class I antigen with the low ACE value of -251 kcal/mol which showed a significant binding. Molecular simulation using normal mode analysis (NMA) illustrated the docking complex as a stable one. Therefore, this observation strongly indicated that our multi epitopes bases peptide vaccine molecule will be an effective candidate for the treatment of the PCa.

Immunoinformatics design of multiepitopes peptide-based universal cancer vaccine using matrix metalloproteinase-9 protein as a target

A new approach toward cancer therapy is the use of cancer vaccine, yet the different molecular bases of cancers, reduce the effectiveness of this approach. In this article, we aim to use matrix metalloproteinase-9 protein (MMP9) which is an essential molecule in the survival and metastasis of all types of cancers as a target for universal cancer vaccine design. The reference sequence of MMP9 protein was obtained from NCBI databases. Furthermore, the B-cell and T cell-related peptides were analyzed using the IEDB website and other related soft wares. The best candidate peptides were then visualized using chimera software. Three peptides were found to be good candidates for interactions with B cells (SLPE, RLYT, and PALPR), while 10 peptides were found as good targets for interactions with MHC1 and another 10 peptides founded suitable for interactions with MHC2 with population coverages of 94.77 and 90.67%, respectively. Finally, the immune response simulation and molecular docking were done using the C-IMMSIM simulator and AutoDock Vina to confirm the effectiveness of the proposed vaccine. By the end of this project: twenty-three peptide-based vaccine was designed for use as a universal cancer vaccine which has a high world population coverage for MHC1 (94.77%) and MHC2 (90.67%) related alleles.

Computational tools for modern vaccine development

Vaccines play an essential role in controlling the rates of fatality and morbidity. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. This review gives an overview of the possible uses of computational tools for vaccine design. Moreover, we have described the initiatives of utilizing the diverse computational resources by exploring the immunological databases for developing epitope-based vaccines, peptide-based drugs, and other resources of immunotherapeutics. Finally, the applications of multi-graft and multivalent scaffolding, codon optimization and antibodyomics tools in identifying and designing in silico vaccine candidates are described.