Immunoinformatic Approach for the Identification of Potential Epitopes Against Stenotrophomonas maltophilia: A Global Opportunistic Pathogen

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Author(s): R. B. Pragathi, Shobana Sugumar*

Journal Name: Letters in Drug Design & Discovery

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Abstract:

Background: Stenotrophomonas maltophilia is an aerobic, non-fermentative, gram-negative, multidrug-resistant, an opportunistic nosocomial pathogen. It is associated with high morbidity and mortality in severely immunocompromised pediatric patients, including neonates. Immunoinformatic analysis paved a new way to design epitope-based vaccines, which results in a potential immunogen at a lower cost, specific immunity, easy to produce, devoid of side effects, less time consuming than conventional vaccines. To date, there is no development of vaccines or antibody-based treatments for S. maltophilia-associated infections.

Introduction: Currently, epitope-based peptide vaccines against pathogenic bacteria have grasped more attention. In our present study, we have utilized various immunoinformatic tools to find a significant epitope that interacts with the maximum number of HLA alleles and the maximum population coverage for developing a vaccine against Stenotrophomonas maltophilia.

Methods: This study has incorporated an immunoinformatic based screening approach to explore potential epitope-based vaccine candidates in Stenotrophomonas maltophilia proteome. In this study, 4365 proteins of the Stenotrophomonas maltophilia K279a proteome were screened to identify potential antigens and could be used as the right candidate for the vaccine. Various immunoinformatic tools were used to predict the binding of the promiscuous epitopes with Major Histocompatibility Complex (MHC) class I molecules. Other properties such as allergenicity, physiochemical, adhesion properties, antigenicity, population coverage, epitope conservancy, and toxicity were analyzed for the predicted epitope.

Results: This study helps in finding the major epitope in Stenotrophomonas infections. Hence the main objective in this research was to screen complete Stenotrophomonas maltophilia proteome to recognize putative epitope candidates for vaccine design. Using computational vaccinology, immunoinformatic tools approach, and several aspects are obligatory to be fulfilled by an epitope to consider as a vaccine candidate. Our findings were promising and showed that the predicted epitopes were non-allergenic and fulfilled the other parameters required for a suitable candidate based on specific physiochemical, antigenic, and adhesion properties.

Conclusion: The epitopes LLFVLCWPL and KSGEGKCGA have shown the highest binding score of −103 and −78.1 kcal/mol with HLA-A*0201 and HLA-B*0702 MHC class I allele, respectively. They were also predicted to be immunogenic and non-allergen. Further various immunological tests both in vivo and in vitro methods to be performed for finding the efficiency of the predicted epitope in the development of a targeted vaccine against Stenotrophomonas maltophilia infection.

Keywords: Stenotrophomonas maltophilia, epitope-based vaccine, immunoinformatics, epitope prediction, docking.

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1570180817999201109202557
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