In-silico design of a therapeutic multi-epitope peptide candidate vaccine against rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease primarily affecting the joints, resulting in inflammation, swelling, and pain, ultimately impacting patients' quality of life and causing premature death. TNF-α, regulated by the NF-κB transcription factor, is a key cytokine implicated in tissue destruction and disease progression. Both TNF-α and the NF-κB receptor ligand (RANKL) are considered significant targets in the treatment of RA. Due to the limitations of current therapeutic strategies in providing safe and effective long-term healing, there is a clear need for novel therapeutic interventions, including vaccine development. This study aims to design an innovative multi-epitope polypeptide vaccine targeting RA through computational methods. To achieve this goal, epitope sequences from TNF-α and RANKL were predicted using three servers. Adjuvants CTxB, PADRE, and B29 were then incorporated with appropriate linkers. The structure underwent several analyses to assess physicochemical properties, immunogenicity, and allergenicity using various servers. The designed protein showed slightly hydrophilic characteristics with a negative Gravy index and a pI of 8.20. Additionally, with an instability index below 40, the protein demonstrates stability, which is crucial for the vaccine's effectiveness. To predict the 3D structure, I-TASSER and trRosetta servers were utilized, and the best models underwent refinement using the GalaxyRefine server. Validation of the refined models was performed by the ProSA-web and Ramachandran plot analysis, and the best model was determined. Ultimately, the results of the conformational B-cell epitope prediction of the final model suggested that the designed vaccine can effectively trigger the humoral immune response. The combination of ten epitopes from TNF-α and RANKL linked with CTxB, PADRE, and B29 adjuvants presented a promising therapeutic candidate vaccine for RA.