Computational Designing of a Multi-Epitope Vaccine Against Streptococcus pyogenes

Group A Streptococcus (GAS), or Streptococcus pyogenes is almost exclusive and greatly adapted human pathogen. It causes a wide array of clinical symptoms, ranging from minor infections of the skin and soft tissues to pharyngitis, meningitis, pneumonia, bacteraemia, cellulitis, puerperal sepsis, and necrotising fasciitis. The risk of S. pyogenes infection is known to be influenced by several host characteristics, including age, underlying diseases like diabetes, varicella, or skin lesions, both chronic and acute, and certain risk behaviours such as use of drugs. Household size and overcrowding are two environmental factors that significantly affect the transmission of S. pyogenes. The majority of cases occur spontaneously in the community, and preventative opportunities are still limited. A large portion of GAS-related mortality is found in low-income areas and communities. Based on aforementioned public health risk, the creation of effective therapeutic vaccines would be an excellent addition to current control measures. The purpose of this work is to address the need for new instruments to aid in the elimination of S. pyogenes infections. The discovery of high antigenic regions in several highly conserved proteins brings us one step closer to developing peptide vaccines capable of influencing the different phases of S. pyogenes infection, providing more effective defence and greater serotype coverage. This study used various techniques of immunoinformatics to design an effective multi-epitope vaccine that produced neutralising antibodies against multiple strains of S. pyogenes.