Immunoinformatics investigation on pathogenic Escherichia coli proteome to develop an epitope-based peptide vaccine candidate

Escherichia coli (E. coli), a gram-negative bacterium, quickly colonizes in the human gastrointestinal tract after birth and typically sustains a long-term, symbiotic relationship with the host. However, certain virulent strains of E. coli can cause diseases such as urinary tract infections, meningitis, and enteric disorders. The rising antibiotic resistance among these strains has heightened the urgency for an effective vaccine. This study employs immunoinformatics and a reverse vaccinology technique to identify prospective antigens and create an efficient vaccine construct. In this study, we reported the "Attaching and Effacing Protein" a novel outer-membrane protein conserved in all pathogenic E. coli strains, based on proteome screening. We developed an in silico multi-epitope vaccine that includes helper T lymphocyte (HTL), cytotoxic T lymphocyte (CTL), B cell lymphocyte (BCL), and pan HLA DR-binding reactive epitope (PADRE) sequences, along with appropriate linkers and adjuvants. Machine Learning algorithms were used to evaluate antigenicity, solubility, stability, and non-allergenicity of the vaccine construct. Additionally, molecular docking analysis revealed that vaccine construct has a strong predicted binding affinity for human toll-like receptors on the cell surface. In this context, laboratory validations are necessary to demonstrate the effectiveness of the possible vaccine design that showed encouraging findings through computational validation.