COMPUTER SIMULATION IN THE DEVELOPMENT OF VACCINES AGAINST COVID-19 BASED ON HLA-SYSTEM ANTIGENS
Genetic variability of population may explain different individual immune responses to the SARS-CoV-2 virus. The use of genome-based technologies makes it possible to develop vaccines by optimizing target antigens. The conventional approach to the development of attenuated or inactivated vaccines sometimes fail to provide potential immunity to the target antigen and has raised safety concerns in many preclinical and clinical trials. Subunit vaccines, such as those predicted by in silico research, can overcome these difficulties. The computer modeling methodology provides the scientific community with a more complete list of immunogenic peptides, including a number of new and cross-reactive candidates. Studies conducted independently of each other with different approaches provide a high degree of confidence in the reproducibility of results. Computer forecasting plays an important role in a quick and cost-effective solution to prevent further spread and ultimately eliminate the pandemic. Most of the effort to develop vaccines and drugs against SARS-CoV-2 is directed towards the thorn glycoprotein (protein S), a major inducer of neutralizing antibodies. Several vaccines have been shown to be effective in preclinical studies and have undergone clinical trials to combat COVID-19 infection. This review presents the profile of in silico predicted immunogenic peptides of the SARS-CoV-2 virus for subsequent functional validation and vaccine development, highlights current advances in the development of subunit vaccines to combat COVID-19, taking into account the experience that has been previously achieved with SARS-CoV and MERS-CoV. Immunoinformatics techniques reduce the time and cost of developing vaccines that together can stop this new viral infection.