Multi-Epitopes DNA Based Feline Immunodeficiency Virus Vaccine Constructs Designed by ImmunoInformatics and Machine Learning Tools as a Surrogate Model for the HIV Vaccine Development

Feline immunodeficiency virus (FIV) is a lentivirus sharing significant structural and pathological similarities to human immunodeficiency virus (HIV), making it a valuable surrogate model for HIV vaccine design and development. Currently, there is no available effective vaccine could protect cats against FIV infection. This study aims to use some artificial intelligence and immunoinformatic to design a novel multi-epitope DNA vaccine targeting some conserved regions of FIV’s gag, pol, and env genes. The mapped B and T-cell epitopes across the key proteins of the FIV genomes were screened for their ability to trigger strong immune responses, while avoiding allergenic or toxic responses and were linked to the immune adjuvant PADRE. Analysis of the vaccine construct revealed a stable, soluble, and biocompatible vaccine construct with a well-folded tertiary structure capable of binding toll-like receptor 9 (TLR9) and eliciting a robust humoral and cellular immune response. These results demonstrate a promising FIV vaccine candidate with potential insight into future directions in next generation HIV vaccines. Further experimental validation is required to confirm the potential protective power of these putative vaccines in the protection of cats against FIV natural field infection.