In Silico Design and Immunoinformatic Evaluation of a Multi-Epitope Vaccine Targeting the TcdE Protein of Clostridioides Difficile
Abstract
Clostridioides difficile infection (CDI) is one of the most relevant healthcare-related conditions, especially among elderly patients in long-lasting antibiotics treatment. Traditional therapy using vancomycin, fidaxomicin and metronidazole only mitigates the symptoms but is not sufficient to prevent the relapse and frequently induces unfavorable changes in the gut. In this paper we have conceptualized and developed a multi-epitope vaccination scheme in response to the holin-like TcdE protein of C. difficile based on novel immunoinformatic and molecular simulation tools. TcdE is essential to the toxin release process causing it to be more functional and safer as a vaccine target than direct toxin-based approaches. Lock-and-key structures involved in predicted B-cell, MHC, class I, and MHC class II epitopes were constructed with an adjuvant and suitable linkers to guarantee the stability of structure and its broad immunogenicity. There was evidence using computational methods to show that the construct had an assurance of non-allergenicity, non-toxicity, antigenicity, as well as desirable solubility. There was structural validation that showed the reliability of the stereochemistry, together with molecular docking of the compound against TLR4 and 500 ns molecular dynamics simulation showed and represented a strong binding affinity and dynamic stability. Immune simulation showed a strong B-cell, helper and cytotoxic T-cell activation, antibody generation, cytokine expression and memory cell development. The codon optimization also showed aptness to be expressed in E. coli, and all these results indicate the promise of the planned TcdE-based vaccine as a potential preventative aid against CDI among the older and high-risk groups.
Fields of Study & Subjects
vaccines and immunoinformatics approaches
Immunotherapy and Immune Responses
Computational Drug Discovery Methods
Publication details
- Journal / source
- SSRN Electronic Journal
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Preprint