Mining Autoimmune-Disorder-Linked Molecular-Mimicry Candidates in <i>Clostridioides difficile</i> and Prospects of Mimic-Based Vaccine Design: An In Silico Approach

Abstract

Molecular mimicry, a phenomenon in which microbial or environmental antigens resemble host antigens, has been proposed as a potential trigger for autoimmune responses. In this study, we employed a bioinformatics approach to investigate the role of molecular mimicry in Clostridioides difficile-caused infections and the induction of autoimmune disorders due to this phenomenon. Comparing proteomes of host and pathogen, we identified 23 proteins that exhibited significant sequence homology and were linked to autoimmune disorders. The disorders included rheumatoid arthritis, psoriasis, Alzheimer’s disease, etc., while infections included viral and bacterial infections like HIV, HCV, and tuberculosis. The structure of the homologous proteins was superposed, and RMSD was calculated to find the maximum deviation, while accounting for rigid and flexible regions. Two sequence mimics (antigenic, non-allergenic, and immunogenic) of ≥10 amino acids from these proteins were used to design a vaccine construct to explore the possibility of eliciting an immune response. Docking analysis of the top vaccine construct C2 showed favorable interactions with HLA and TLR-4 receptor, indicating potential efficacy. The B-cell and T-helper cell activity was also simulated, showing promising results for effective immunization against C. difficile infections. This study highlights the potential of C. difficile to trigger autoimmunity through molecular mimicry and vaccine design based on sequence mimics that trigger a defensive response.

Keywords

• <i>Clostridioides difficile</i>
• molecular mimicry
• autoimmunity
• vaccine design
• bioinformatics
• immune response