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Helicobacter pylori is the cunning bacterium that can live in the stomachs of many people without any symptoms, but gradually can lead to gastric cancer. Due to various obstacles, which are related to anti-H. pylori antibiotic therapy, recently developing an anti-H. pylori vaccine has attracted more attention. In this study, different immunoinformatics and computational vaccinology approaches were employed to design an efficient multi-epitope oral vaccine against H. pylori. Our multi-epitope vaccine is composed of heat labile enterotoxin IIc B (LT-IIc) that is used as a mucosal adjuvant to enhance vaccine immunogenicity for oral immunization, cartilage oligomeric matrix protein (COMP) to increase vaccine stability in acidic pH of gut, one experimentally protective antigen, OipA, and two hypothetical protective antigens, HP0487 and HP0906, and "CTGKSC" peptide motif that target epithelial microfold cells (M cells) to enhance vaccine uptake from the gut barrier. All the aforesaid segments were joined to each other by proper linkers. The vaccine construct was modeled, validated, and refined by different programs to achieve a high-quality 3D structure. The resulting high-quality model was applied for conformational B-cell epitopes selection and docking analyses with a toll-like receptor 2 (TLR2). Moreover, molecular dynamics studies demonstrated that the protein-TLR2 docked model was stable during simulation time. We believe that our vaccine candidate can induce mucosal sIgA and IgG antibodies, and Th1/Th2/Th17-mediated protective immunity that are crucial for eradicating H. pylori infection. In sum, the computational results suggest that our newly designed vaccine could serve as a promising anti-H. pylori vaccine candidate.

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The clinical applications of therapeutic enzymes are often limited due to their immunogenicity. B-cell epitope removal is an effective approach to solve this obstacle. The identification of hot spot epitopic residues is a critical step in the removal of protein B-cell epitope. Hereof, computational approaches are a suitable alternative to costly and labor-intensive experimental approaches. Arginine deiminase, a Mycoplasma arginine-catabolizing enzyme, is in the clinical trial for treating arginine auxotrophic cancers, especially hepatocellular carcinomas and melanomas through depleting plasma arginine and causing cell starvation. In this study, arginine deiminase from Mycoplasma hominis (MhADI) was computationally analyzed for recognizing and locating its immune-reactive regions. The 3D structure of the bioactive form of MhADI was modeled. The B-cell epitope mapping of protein was performed using various servers with different algorithms. Six segments: 31-40, 48-55, 131-140, 196-206, 294-314, and 331-344 were predicted to be the consensus immunogenic regions. The modification of epitopic hot spot residue was performed to reduce immune-reactiveness. The hot spot residue was selected considering a high B-cell epitope score, convexity index, surface accessibility, flexibility, and hydrophilicity. The structure stability of native and mutant proteins was evaluated through molecular dynamics simulation. The E304L mutein was suggested as a lower antigenic and stable enzyme derivative.

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