Scholarlink

<p>Tuberculosis (TB), the biggest cause of death from any known infectious disease, has been a problem for the world’s health system for many years. The only approved vaccine is BCG and now a number of vaccines are undergoing clinical trials. The newly recognised mRNA vaccines can provide a good alternative to the traditional vaccine. Therefore, the goal of this work is to use computational techniques to build a multi-stage tuberculosis mRNA vaccine. Nine multistage-expressing <i>Mycobacterium tuberculosis</i> (Mtb) proteins that have been linked to cell entry, pathogenesis, and dormancy regulon were used. Eighteen promiscuous, proinflammatory, non-toxic antigenic epitopes that bound to both MHC classes (I and II) were chosen from nine proteins. Co-translational structural components (5′m7G cap, UTRs, Kozak sequence, Poly A tail) were incorporated into the design of the mRNA vaccine, which was predicted to be stable. Additionally, molecular docking reveals the vaccine candidate’s interaction with TLR2 and TLR4 immunological receptors by establishing a Leucine-Rich-Repeats (LRR) specific interface. Molecular dynamics (MD) simulations were also performed to evaluate the structural stability and dynamical behaviour of the vaccine candidate in complex with TLR2 and TLR4. Following the simulations, binding free energy calculations were conducted using the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) method, which indicated stable thermodynamic binding of the vaccine candidate with both the receptors. Immune simulation tests using C-Immsim predicted that the translated vaccine construct was immunogenic and showed population coverage of 99.98%. Overall, it was anticipated that this multi-stage expressing mRNA vaccine would be highly immunogenic, stable, safe, and antigenic. Our approach to develop an immunoinformatics-based mRNA vaccine appears promising against tuberculosis; nonetheless, experimental confirmation is required.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Computational design and immunoinformatic validation of a multistage mRNA vaccine candidate against Mycobacterium tuberculosis

  • 期刊论文

  • 发表日期：

    2026年6月20日

查看全文
  • Manika Sharma,
  • Parul Bhatt,
  • Medha Singh,
  • Kiran Bharat Lokhande,
  • Shailendra Asthana,
  • Monika Sharma,
  • Sadhna Sharma

摘要

Tuberculosis (TB), the biggest cause of death from any known infectious disease, has been a problem for the world’s health system for many years. The only approved vaccine is BCG and now a number of vaccines are undergoing clinical trials. The newly recognised mRNA vaccines can provide a good alternative to the traditional vaccine. Therefore, the goal of this work is to use computational techniques to build a multi-stage tuberculosis mRNA vaccine. Nine multistage-expressing Mycobacterium tuberculosis (Mtb) proteins that have been linked to cell entry, pathogenesis, and dormancy regulon were used. Eighteen promiscuous, proinflammatory, non-toxic antigenic epitopes that bound to both MHC classes (I and II) were chosen from nine proteins. Co-translational structural components (5′m7G cap, UTRs, Kozak sequence, Poly A tail) were incorporated into the design of the mRNA vaccine, which was predicted to be stable. Additionally, molecular docking reveals the vaccine candidate’s interaction with TLR2 and TLR4 immunological receptors by establishing a Leucine-Rich-Repeats (LRR) specific interface. Molecular dynamics (MD) simulations were also performed to evaluate the structural stability and dynamical behaviour of the vaccine candidate in complex with TLR2 and TLR4. Following the simulations, binding free energy calculations were conducted using the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) method, which indicated stable thermodynamic binding of the vaccine candidate with both the receptors. Immune simulation tests using C-Immsim predicted that the translated vaccine construct was immunogenic and showed population coverage of 99.98%. Overall, it was anticipated that this multi-stage expressing mRNA vaccine would be highly immunogenic, stable, safe, and antigenic. Our approach to develop an immunoinformatics-based mRNA vaccine appears promising against tuberculosis; nonetheless, experimental confirmation is required.