# In Silico Design and Characterization of a Multi-Epitope Vaccine Candidate Against Mycoplasma pneumoniae Using a Reverse Vaccinology Strategy

## TL;DR

This paper presents a new in silico-designed multi-epitope vaccine candidate against Mycoplasma pneumoniae, aiming to offer broad protection against respiratory infections.

## Contribution

The study introduces a novel in silico vaccine design strategy using reverse vaccinology for M. pneumoniae.

## Key findings

- The multi-epitope vaccine (MEV) showed ideal physicochemical properties and high antigenicity.
- The vaccine demonstrated strong binding interactions with toll-like receptors and was non-toxic.
- Codon optimization improved the vaccine's GC content and codon adaptation index.

## Abstract

Mycoplasma pneumoniae (M. pneumoniae) is the crucial factor of global acquired respiratory infections. Currently, there are no specific disease modification treatments or vaccines available, and the vaccine development for this pathogen lags behind due to the complexity and variability of its antigens. A novel vaccine with broad-spectrum characteristics is essential to provide comprehensive protection against continuously evolving wild-type strains. Here, a broad-spectrum muti-epitope vaccine against M. pneumoniae had been designed through immunoinformatics methods. To ensure its broad-spectrum, we generated consistent sequences from all the antigen proteins of different strains, and then identified potential T cell epitopes. The multi-epitope vaccine (MEV) of M. pneumoniae incorporated 16 CTLs and 7 HTLs from the HMW1–3 and p1 adhesin proteins, which comprised 458 amino acids with adjuvant. The vaccine evaluation showed that the MEV had ideal physicochemical properties, high antigenicity, high immunogenicity, and was non-toxic. Furthermore, there was a strong and stable binding interaction between this vaccine and the toll-like receptors, which could be supported by the normal mode analysis. Finally, codon optimization resulted in the optimal GC content and higher CAI value. The vaccine candidate is expected to induce strong cellular immune responses and may provide protective immunity against the pathogen. We provided a novel in silico vaccine design strategy for vaccine design, which could provide a technical framework for the development of vaccines against other pathogens.