Development of an Env based HIV mRNA vaccine through immunoinformatics and computational modeling.

The Human Immunodeficiency Virus (HIV) is a significant challenge to the global healthcare system. Recent efforts to develop an effective immune-stimulatory vaccine for HIV-1 have attracted considerable attention. This study aims to design an mRNA vaccine for HIV using its Env region. By using different servers and filtering algorithms, we effectively select epitopes for B-cells, helper T lymphocytes (HTL), and cytotoxic T lymphocytes (CTL). Seventeen epitopes were found suitable for the vaccine, including three B-cell epitopes, seven cytotoxic T lymphocytes (CTLs), and seven helper T lymphocytes (HTLs). This vaccine has 364 amino acids, possesses a theoretical isoelectric point of 8.98, and has a practical GRAVY score of -0.865. The Ramachandran plot indicated remarkable stability, with 87.7% of residues located within the allowed and 10.8% additionally allowed regions. By optimizing codons computationally and cloning them into prokaryotic vectors, we effectively created Escherichia coli hosts with improved expression systems. Molecular dynamics simulations revealed that the vaccine components have the highest binding affinity for Toll-like receptor 3 (TLR3) (-318.19 kj/mol). These in-silico findings need experimental confirmation, notwithstanding the vaccine model's effectiveness in inducing cellular and humoral immune responses.