The insilico design of ovarian cancer vaccine using bioinformatics tools

Abstract

Introduction: Cancer is one of the deadliest diseases in recent decades. Among the various types of cancer, ovarian cancer is one of the most common types of cancer in women. Designing a cancer vaccine takes a lot of time and money, but using bioinformatics design tools can save a lot of time, energy and money. Objective: Determination of antigens and their optimal epitopes and design of ovarian cancer vaccine using bioinformatics tools Method: Four epitopes with the highest scores in terms of potency in activating B-cell, T Cytotoxic and T-Helper of MAGEA4, MAGE-C1, and OY-TES-1 antigens, which are highly expressed in ovarian cancer, using the method Bioinformatics were selected. Then, after connecting the selected epitopes and adjuvant to each other, designed non-allergenic protein (with a score of 0.4812), half-life of more than 10 hours in E. Coli, has an aliphatic index of about 86 and has the ability to interact with water molecules. Has. After modifying the three-dimensional structure of the protein with the help of GalaxyRefine server, the percentage of relevant amino acids in the authorized areas increased from 54% to 86%, and the amino acid in the unauthorized areas decreased from 2.7% to 1.6%. Results: The results of this study show that our selective sequence of three antigens including MAGE-C1, OY-TES-1, MAGE-A4 each of which select epitopes have the most optimal percentile rank and ic50 with linkers and adjuvants. Suitably coupled. Conclusion: Given that the proposed peptide sequence could theoretically be used to design vaccines, it is hoped that future studies using these results will lead to the development of effective vaccines in overcoming cancer cells.