<i>In Silico</i> Identification of Immunogenic ERv 53-63 Epitope; A Potential Candidate for Tuberculosis Vaccine Design

Background and Aim: Tuberculosis (TB), caused by Mycobacterium (M.) tuberculosis (MTB), remains a major global health burden and the Bacillus Calmette-Gurin (BCG) vaccine shows limited efficacy, particularly in adults.This highlights the urgent need for novel vaccine strategies.In silico approaches offer efficient, cost-effective tools for predicting immunogenic epitopes, thereby accelerating peptide-based vaccine development.This study aimed to identify and characterize the ERv 53-63 epitope, a novel candidate compared to previously studied sequences, as a potential B-cell-targeting epitope for TB vaccine design. Materials and Methods:Epitope selection was based on antigenic regions within the ERv protein sequence.ERv 53-63 was chosen due to higher predicted antigenicity and favorable binding potential.VaxiJen was used for antigenicity assessment, AllerTOP v2.0 for allergenicity, and ToxinPred for toxicity evaluation.Structural docking simulations were conducted using PyMOL, with a human B-cell receptor (PDB ID: 5DRW) as docking target to evaluate epitope-receptor interaction.Results: ERv 53-63 demonstrated high antigenicity (VaxiJen score: 0.9599).It was predicted as non-allergenic and non-toxic, and exhibited strong binding affinity with B-cell receptor (interaction energy: -877.8 kcal/mol), indicating stable complex formation. Conclusion: These findings support ERv 53-63 as a novel and promising in silico-derived B-cell epitope, outperforming prior candidates such as ERv 105-118.It holds strong potential for peptide-based TB vaccine development.Further in vitro and in vivo studies are recommended to validate its immunogenicity and safety.