Integrative immunoinformatics for Mycobacterial diseases in R platform

Area of Science:

• Immunoinformatics
• Genomic analysis
• Vaccine development

Background:

• Pathogenic mycobacteria cause tuberculosis, leprosy, and other infections, necessitating new vaccines and therapeutics.
• Genomic sequencing offers opportunities for identifying novel vaccine candidates.
• Enhanced Reverse Vaccinology (RV) improves vaccine candidate prediction by reducing allergenicity and host cross-reactivity.

Purpose of the Study:

• To develop MycobacRV, an immunoinformatics database of known and predicted mycobacterial vaccine candidates.
• To identify and prioritize robust vaccine candidates against pathogenic mycobacteria.
• To provide tools for searching vaccine candidates and analyzing epitope conservation.

Main Methods:

• Whole genome sequences of 22 pathogenic and 1 non-pathogenic mycobacterial species were analyzed.
• Adhesins and adhesin-like proteins were identified using SPAAN and screened for extracellular/surface localization via PSORTb.
• Protein sequences were analyzed using 21 algorithms for various properties, including epitopes and allergenicity, with data processed in R.

Main Results:

• A non-redundant set of 233 most probable vaccine candidates was derived using enhanced RV principles.
• The conservation of potential epitopes was assessed across orthologs, referencing Mycobacterium tuberculosis H37Rv.
• The mycobacrvR package in R facilitates vaccine candidate search and epitope conservation analysis.

Conclusions:

• MycobacRV provides a valuable resource for researchers developing vaccines against mycobacterial diseases.
• The enhanced RV approach successfully identified promising vaccine candidates with improved safety profiles.
• Accessible tools are available for further research and development of mycobacterial vaccines.