In-silico identification and characterisation of candidate B-cell epitopes of SPA ClfA, ClfB FnBPA and FnBPB for peptide based vaccine design for control of staphylococcus aureus infection

Abstract

Staphylococcus aureus remains a significant global health threat due to its capacity to cause severe infections and its growing resistance to antibiotics. Peptide-based vaccines targeting surface adhesins offer a promising alternative approach. This study employed comprehensive immunoinformatic analyses to identify and characterize candidate linear B-cell epitopes from five critical Staphylococcus aureus surface proteins: Spa, ClfA, ClfB, FnBPA, and FnBPB. Protein sequences were retrieved from the NCBI database and subjected to multiple B-cell epitope prediction algorithms, including BepiPred-2.0 and ABCpred, to delineate linear epitopes. Predicted epitopes were further evaluated for antigenicity (using VaxiJen with a threshold of 0.4), allergenicity (Algpred), toxicity (ToxinPred), and physicochemical properties (ProtParam) to ensure optimal immunogenicity and safety. Although several epitopes exhibited antigenicity scores above the 0.4 threshold, non-allergenic and non-toxic profiles, and favourable physicochemical stability individually, the constructed multiepitope chimera was found to be unstable based on stability prediction analyses. These findings highlight promising epitope candidates for vaccine design against Staphylococcus aureus, while underscoring the need for further optimization of the chimera construct. Experimental validation and structural refinement studies are recommended to improve stability and assess protective efficacy.