Reverse Vaccinology and Immunoinformatics Strategy to Screen Oncogenic Proteins and Development of a Multiepitope...

Area of Science:

• Oncology
• Vaccinology
• Computational Biology

Background:

• Oral cancer presents a high mortality rate despite existing treatments.
• There is a critical need for more effective and targeted oral cancer therapies.
• Multi-epitope vaccines offer a promising strategy for cancer prevention and treatment.

Purpose of the Study:

• To design a novel multi-epitope peptide vaccine for oral cancer prevention using computational methods.
• To identify potential vaccine targets and immunogenic epitopes through immunoinformatics.
• To evaluate the vaccine construct's binding affinity and immune response potential.

Main Methods:

• Employed a reverse vaccinology approach integrated with machine learning and immunoinformatics tools.
• Identified LYN Proto-Oncogene and AKT1 as key targets based on their roles in cancer regulation.
• Selected B and T cell epitopes with favorable physicochemical properties, non-toxicity, and high antigenicity.

Main Results:

• LYN and AKT1, crucial protein kinases in cancer, were identified as suitable vaccine targets.
• A vaccine construct incorporating selected immune epitopes and a Toll-like receptor 4 (TLR4) agonist adjuvant demonstrated strong binding affinity to TLR4.
• Immune simulations predicted robust immune cell activation and a sustained IgG/IgM response for up to a year.

Conclusions:

• The designed multi-epitope peptide vaccine exhibits significant immunogenic potential against oral cancer.
• The vaccine is predicted to induce both cell-mediated and humoral immunity.
• This computational approach provides a foundation for developing a novel oral cancer vaccine.