Advancements in Multi-Epitope Vaccine Development for Myasthenia Gravis: An Innovative Approach

Background: Myasthenia gravis (MG) is an autoimmune neuromuscular disorder caused by theproduction of autoantibodies against the acetylcholine receptor (AChR) at the neuromuscularjunction. Current treatment strategies for MG involve immunosuppressive drugs, which are associatedwith adverse effects, and may not be effective in all patients. Therefore, there is a needfor new therapeutic approaches. Vaccines targeting the immune system have shown promisingresults in treating various neurological disorders, including MG. In this study, we aimed to designa multi-epitope vaccine against MG using advanced immunoinformatics and artificial intelligence(AI) techniques.Methods: We utilized various bioinformatics tools to identify and select immunogenic epitopesfrom proteins associated with MG, including AChR, muscle-specific kinase (MuSK), andlow-density lipoprotein receptor-related protein ۴ (LRP۴). The selected epitopes were linked usinga linker, namely AAY, EAAAK, and GPGPG to complete a final sequence. Prediction of thetertiary structure of the chimeric protein via MODELLER, a Galaxy refines database used torefine the structure. GROMACS was utilized to conduct molecular dynamics (MD) simulations,in order to evaluate the stability of the chimeric protein in the blood. Furthermore, the solubility,allergenicity, and antigenicity of the vaccine were anticipated. Furthermore, to enhance the vaccineimmune response, a ۵۰S ribosomal protein L۷/L۱۲, which stimulates human innate immunereceptors, was linked to the N-terminus. Finally, to simulate the immune response of the vaccine,the C-ImmSim server was employed.Results: Select epitopes based on a combination of factors, including low percentile rank,strong binding scores, and favorable immunological characteristics. The chimeric protein waswell-structured and stable, as confirmed by several analyses, including the ERRAT score andRamachandran plot. The simulation of the vaccine binding affinity to TLR-۲ and TLR-۴ andthe stability of the complex were achieved through energy minimization and molecular dockingtechniques. MD simulations confirmed that the vaccine-TLR complex maintained a stable conformationthroughout the study. The simulation of the immune response demonstrated that thevaccine has the potential to induce a robust and effective immune response from both the innateand adaptive immune systems. Moreover, the vaccine antigenicity and allergenicity predictionswere acceptable, suggesting that the human body would likely tolerate the vaccine.Conclusion: Our study has led to the development of a groundbreaking new MG vaccine that utilizesa novel combination of antigens and advanced scientific techniques like immunochemistryand machine learning. This innovative vaccine can provide immunity against a range of proteinsrelated to MG, and it has the potential to be an affordable and effective solution. Our findingsdemonstrate that computational techniques can be successfully employed to create vaccines for aneuroimmune disorder such as MG, and this methodology may apply to other autoimmune disorders.However, more extensive testing is necessary to confirm the vaccine›s efficacy in preclinicaland clinical trials.

کلیدواژه ها:

Myasthenia gravis ، chimeric vaccine ، Immunoinformatics ، bioinformatics