Development of epitope-based vaccine to prevent Marburg virus infection: an in silico approach

Marburg virus (MARV) is one of the deadliest zoonotic viruses, causing severe hemorrhagic fever in humans with high mortality rates. The development of an effective vaccine is crucial to prevent potential Marburg virus outbreaks. In this study, an in silico approach was employed to design an epitope-based vaccine to prevent MARV infections. The MARV proteins nominating NP, VP24, VP35, VP30, VP40, GP & Polymerase L was analyzed for antigenicity and non-allergenicity prediction, among these proteins VP30 protein has a 0.5636 (Probable Antigen) score and it was non-allergen. For that reason, VP30 was selected for further in silico analysis. After analysis it is found that the top ranked T–cell (MHC-I) epitopes LSKPPPPPK, ESSPTNHIPR, TQLPSKPHY, SPQDCGSPSL, FEAALWQGW, T-Cell (MHC-II) epitopes IHLDKGGQF, INTMTELHM, VTPTIYHET, YTNYHPRAR, YTGIHLDKG was epitopes & B-Cell epitopes SEIGKLDET, IHLDKGGQF, MNHENLPQDQNGV, PTCNRDHDLDNLTN was found non-toxic and non-allergen. The T-Cell (MHC-I)epitope TQLPSKPHY,T-Cell (MHC-II)epitope YTNYHPRAR & B-Cell epitope SEIGKLDET was found highly antigenic, non-toxic as well as non-allergen and it was selected for molecular docking analysis. The T-Cell (MHC-I) epitope TQLPSKPHY,T-Cell (MHC-II)epitope YTNYHPRAR shows strong structural similarity and potential binding affinity with antibody. The B-Cell epitope SEIGKLDET shows poor affinity towards antibody. In silico analysis indicate that both T-Cell epitopes becomes an effective peptide vaccine to prevent MARV infection. Our findings highlight the promise of in silico vaccine design in accelerating the development of vaccines against MARV, a highly pathogenic virus with no effective cure currently availabl

Computational Efficacy of Artificial Intelligence Model for in Silico Vaccine Development

Bioinformatics is an interdisciplinary branch of science that develops methods and software tools for understanding biological data. Bioinformatics include both the power of biological concept and computational method to solve biological problem. It also bridged biological field with speed and accuracy of computer. Pre-design of vaccines by using artificial intelligence model for future upcoming viruses. Using AI throughout the vaccine development process to ensure that virus/pathogen vaccine met the needs of individuals without spending much time. A piece of genetic code that is capable of copying itself and typically has a detrimental effect on body, the pre-design vaccines will be available on one click no need for direct trials on humans. The model gives the predicted information about the upcoming risks for transmitting the disease in future generations by using artificial intelligence. The model is based on artificial intelligences and bioinformatics filed, all data will be presented and analyze simultaneously by the model and will efficiently build the vaccine molecule against the virus. The model provides highest accuracy and speed to sort out the vaccine