Salem Almujri Poster 2025

Dr Salem Almujri

King Khalid University, Saudi Arabia

Immunoinformatics-Driven Identification of Broadly Protective TB Vaccine Candidates

Poster Abstract

Mycobacterium tuberculosis (MTB), the leading infectious killer worldwide, is the causative agent of tuberculosis (TB). Despite the century-old Bacillus Calmette-Guérin (BCG) vaccine, its limited efficacy, the emergence of drug-resistant MTB strains, and the high cost and prolonged duration of current treatments underscore the urgent need for more effective vaccines. Research highlights the critical role of T cells in combating MTB infection, with growing evidence suggesting that antibodies also contribute to immunity. For T cell activation, antigens must be presented as peptides (epitopes) via the Human Leukocyte Antigen (HLA) system, also known as the Major Histocompatibility Complex (MHC). Advances in immunoinformatics and machine learning have facilitated the identification of potential vaccine candidates by predicting and analyzing HLA-associated epitopes.

In this study, we screened 35 highly expressed MTB antigens against the most prevalent HLA alleles worldwide: HLA-DRB1 (n=42), HLA-A (n=45), HLA-B (n=60), and HLA-C (n=32). We identified 14 antigens predicted to be presented by all screened alleles, ensuring broad population coverage. Computational simulations using C-ImmSim further confirmed their potential to induce robust humoral and cellular immune responses, supporting its promise as a next-generation TB vaccine candidate. Given these findings, further in vivo validation is warranted to assess its immunogenicity and protective efficacy in preclinical models, paving the way for potential clinical development.