A Design of mRNA-Based COVID-19 Vaccine Through Fuzzy Neural Network

The worldwide effort to develop a vaccine against SARS-CoV-2 has led to a revolution in vaccinology by introducing a completely new class of vaccines messenger RNA (mRNA) vaccine. The mRNA-based vaccine is a singular molecule made in the lab that teaches the cells to produce an antigen to trigger the immune response against the fake infection. However, new variants of SARS-CoV-2 may consist of an unprecedented set of genetic mutations including a sampling of earlier variants in addition to the other unknown mutations on the spike protein that may bind a part of the virus to human cells like a grappling hook. A common paradigm in designing a vaccine is to create a fixed architecture in the hope that it can make connections between the vaccine and mutations. In this paper, we propose a COVID-19 RNA-based vaccine in four modules: SARS-CoV-2 profile, mRNA-based vaccine design, interaction box, and neural codon optimization. We use epitopes' perception to collectively analyze mutations for designing mRNA-based vaccines and optimize the vaccine through neural codon optimization. In the proposed vaccine, the structural variation of the inhibitor is changed with the interaction of COVID-19 variants. To evaluate, the proposed vaccine is applied to the real data set. The results demonstrate that the proposed vaccine can provide high levels of protection against various virus mutations in comparison. Even with the challenging New3 mutation, the proposed vaccine still provided a good 78% protection with two doses of vaccination.