Developing a Chimeric-Vaccine against Epilepsy via Immunoinformatics and Artificial Intelligence techniques

Background and aims: Epilepsy is a chronic neurological disorder characterized by recurrentseizures caused by abnormal electrical activity in the brain. Despite advances in pharmacologicaltreatments, a significant proportion of patients with epilepsy do not achieve adequate seizurecontrol, and there is a need for novel therapeutic strategies. Immunotherapeutic approaches, includingvaccines, have shown promising results in treating various diseases, specifically neurologicalDisorders. Pathophysiology of epilepsy involves alterations in the expression or functionof various proteins and immune system dysfunction, such as neuroinflammation and activationof immune cells. In this study, we propose a novel approach to design a multi-epitope chimericvaccine against epilepsy, utilizing advanced Immunoinformatics and AI techniques. Specifically,we aim to identify and incorporate epitopes from proteins associated with epilepsy, includingvoltage-gated potassium channels, glutamate receptors, and synaptic vesicle proteins, into a chimericvaccine that elicits a Th۲-biased immune response.Methods: Epitopes from various proteins associated with epilepsy were identified using bioinformaticstools. Predictions of MHC-۱, MHC-۲, and CTL epitopes were made, and the most immunogenicepitopes were selected. These epitopes were linked together using a linker to form achimeric protein. The ۳D structure of the chimeric protein was modeled using MODELLER, andthe structure was refined using online servers and manual applications. Molecular dynamics (MD)simulations were performed using GROMACS to assess the stability of the chimeric protein thatdocked with TLR۹. The immune response to the vaccine was simulated using the C-ImmSimserver.Results: The newly developed chimeric vaccine exhibited a well-structured design, with an ERRAToutcome that exceeded ۸۰%, indicating a high level of stability. Additionally, the Ramachandranplot analysis revealed that more than ۹۰% of the amino acid residues were in favorable andpermissible locations. Molecular docking was used to bind the designed vaccine with TLR-۹.Molecular dynamics simulations further confirmed that the chimeric protein maintained a stableconformation throughout the study. The vaccine›s solubility, antigenicity, and allergenicitypredictions were all acceptable, suggesting that the human body will likely well-tolerated thevaccine. Moreover, simulations assessing the immune response elicited by the chimeric vaccinedemonstrated a positive reaction from both the innate and adaptive immune systems, indicatingthat the vaccine has the potential to induce a robust and effective immune response. The vaccineprotein was effectively cloned into the pET-۲۱ b(+) / MEV vector, as confirmed by the resultsobtained from SnapGene cloning.Conclusion: Using advanced AI and immunoinformatics methods, we have created a pioneeringchimeric vaccine for epilepsy that targets multiple epitopes. This vaccine has the potential toprovide immunity against a wide range of epilepsy-related proteins and offers an economical andrapid approach to vaccine development. Our results establish the plausibility of utilizing computationalapproaches to design vaccines for neurological disorders like epilepsy. Further validationin preclinical and clinical trials is necessary to ensure the vaccine’s effectiveness.

کلیدواژه ها:

Epilepsy ، chimeric vaccine ، Immunoinformatics ، bioinformatics