Leveraging ribosomal RNA as a molecular target for vaccine development against Acinetobacter baumannii

Background

Acinetobacter baumannii, a nosocomial pathogen, has emerged as a major clinical threat due to its ability to resist a broad range of antibiotics, contributing to the increased morbidity and mortality in hospital settings. This characteristic of Acinetobacter baumannii as a multiple-drug resistant (MDR) organism poses a critical global health challenge, necessitating an urgent need for alternative therapeutic strategies, such as vaccine development, as a preventive measure. In this study, we employ the method of reverse vaccinology and immunoinformatic tools to design a novel rRNA-based vaccine targeting the 16S and 23S rRNA of Acinetobacter baumannii.

Results

16S and 23S rRNA sequences of Acinetobacter baumannii were retrieved from the National Center for Biotechnology Information database (NCBI). The B and T cells’ epitopes were predicted from these retrieved sequences using bioinformatics tools. The epitopes generated were further analyzed for antigenicity, toxicity, and allergenicity. The epitopes that passed these screenings, including key structural elements, were used in the design of the vaccine. The vaccine constructs were further assessed for their physicochemical properties and dynamics. Structural modeling and molecular docking studies confirmed effective binding to Toll-like receptor 4 (TLR-4), while immune simulations demonstrated the potential to elicit robust and durable immune responses.

Conclusions

This study demonstrates the potential of reverse vaccinology and immunoinformatics approaches in designing a novel rRNA-based vaccine targeting the 16S and 23S rRNA of Acinetobacter baumannii. By identifying highly antigenic, non-toxic, and non-allergenic epitopes and incorporating them into a structurally optimized rRNA-based vaccine construct, we present a promising candidate capable of eliciting strong immune responses. However, limitations such as the unavailability of datasets, especially on the 5S rRNA region in the databases, are a roadblock that needs to be addressed.