In silico design of a vaccine candidate based on autotransporters and HSP against the causal agent of shigellosis, Shigella flexneri

Abstract

Shigellosis is a diarrheal disease that causes high mortality every year, especially in children, elderly and immunocompromised patients. Recently, resistance strains to antibiotic therapy are in the rise and the World Health Organization prioritizes the development of a safe vaccine against the most common causal agent of shigellosis, Shigella flexneri. This pathogen uses autotransporter proteins such as SigA, Pic and Sap to increase virulence and some of them have been described as highly immunogenic proteins. In this study, we used immune-informatics analysis to identify the most antigenic epitope as a vaccine candidate on three passenger domains of auto-transporter proteins encoded on the pathogenic island SHI-1, to induce immunity against S. flexneri. Epitope identification was done using various servers such as Bepipred, Bcepred, nHLAPRED, NetMHCII, Rankpep and IEDB and the final selection was done based on its antigenicity using the VaxiJen server. Moreover, to enhance immunity, the GroEL adjuvant was added to the final construct as a Toll-like receptor 2 (TLR2) agonist. On the other hand, to predict the tertiary structure, the I-TASSER server was used, and the best model was structurally validated using the ProSA-web software and the Ramachandran plot. Subsequently, the model was refined and used for docking and molecular dynamics analyses with TLR2, which demonstrated an appropriate and stable interaction. In summary, a potential subunit vaccine candidate, that contains B and T cell epitopes with proper physicochemical properties was designed. This multiepitope vaccine is expected to elicit robust humoral and cellular immune responses and vest protective immunity against S. flexneri.