Title:Immunoinformatic Approach for the Identification of Potential Epitopes Against Stenotrophomonas maltophilia: A Global Opportunistic Pathogen
Volume: 18
Issue: 5
Author(s): Pragathi Ravilla Basker and Shobana Sugumar*
Affiliation:
- Department of Genetic Engineering, School of Bioengineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur-603203, Kanchipuram, Tamilnadu,India
Keywords:
Stenotrophomonas maltophilia, epitope-based vaccine, immunoinformatics, epitope prediction, docking, HLA alleles.
Abstract:
Background: Stenotrophomonas maltophilia is an aerobic, non-fermentative, gram negative,
multidrug resistant and opportunistic nosocomial pathogen. It is associated with high morbidity
and mortality in severely immunocompromised paediatric patients, including neonates. Immunoinformatic
analysis paved a new way to design epitope-based vaccines which resulted in a potential
immunogen with advantages such as lower cost, specific immunity, ease of production, devoid
of side effects, and less time consumption than conventional vaccines. Till date, there is no development
in the 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
prominent epitope that interacts with the maximum number of HLA alleles and also with 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 that could be used as a good 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
properties, adhesion properties, antigenicity, population coverage, epitope conservancy
and toxicity were analysed for the predicted epitope.
Results: This study helps in finding the prominent 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 and
immunoinformatic tools approach, several aspects are obligatory to be fulfilled by an epitope to be
considered as a vaccine candidate. Our findings were promising and showed that the predicted epitopes
were non-allergenic and fulfilled other parameters required for being a suitable candidate
based on certain physio-chemical, 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-allergenic. Further various immunological tests,
both in vivo and in vitro methods, should be performed for finding the efficiency of the predicted
epitope in the development of a targeted vaccine against Stenotrophomonas maltophilia infection.
