Immunoinformatics-Based Multi-Epitope Vaccine Targeting Helicobacter Pylori

Area of Science:

• Computational vaccinology
• Immunoinformatics
• Molecular biology

Background:

• Rising global incidence of Helicobacter pylori-related diseases, including gastric cancer.
• Urgent need for effective preventive vaccines against H. pylori.
• Exploration of immunoinformatics for novel vaccine development.

Purpose of the Study:

• Design a multi-epitope subunit vaccine for H. pylori using immunoinformatics.
• Predict epitopes from flagellin B and urease B proteins.
• Integrate cholera toxin B subunit (CTB) as a mucosal adjuvant and validate the construct computationally.

Main Methods:

• Utilized immunoinformatics for vaccine design.
• Epitope prediction from flagellin B and urease B.
• Integration of CTB adjuvant and computational validation (VaxiJen, Phyre2, MolProbity, HDOCK).

Main Results:

• Developed a 406 amino acid vaccine construct with high antigenicity (score 1.0084), non-allergenic/toxic properties, and stability.
• Structural analysis showed favorable Ramachandran regions (99.1%) and allowed regions (100.0%).
• Superior docking score with TLR5 (-309.05) compared to TLR2, with identified CTL, HTL, and LBL epitopes.

Conclusions:

• Presents a computationally optimized vaccine design with potential for H. pylori immune response.
• Findings are theoretical and require in vitro and in vivo experimental validation.
• Further validation is crucial for assessing immunological relevance, safety, and efficacy prior to clinical application.