AntigenBoost: enhanced mRNA-based antigen expression through rational amino acid substitution

Area of Science:

• Immunology
• Bioinformatics
• Vaccine Development

Background:

• Messenger RNA (mRNA) vaccines have revolutionized immunology and public health, notably during the COVID-19 pandemic.
• Optimizing antigen expression from mRNA is critical for developing effective vaccines.
• Destabilizing dipeptides can impede ribosomal translation, limiting mRNA vaccine performance.

Purpose of the Study:

• To develop a bioinformatics tool, AntigenBoost, for optimizing mRNA antigen expression.
• To identify and address destabilizing dipeptides that hinder translation in mRNA vaccines.
• To enhance the in vitro expression of antigens for mRNA vaccine applications.

Main Methods:

• Development of the AntigenBoost bioinformatics tool.
• Identification of destabilizing dipeptides within target antigens.
• Application of a two-dimensional scoring system for amino acid substitution strategies.
• Bioinformatic analysis combined with experimental validation.

Main Results:

• Successfully enhanced in vitro expression of mRNA-derived Respiratory Syncytial Virus fusion glycoprotein.
• Significantly improved expression of mRNA-derived Influenza A Hemagglutinin antigen.
• Demonstrated that a single amino acid substitution boosted the immune response in mice.

Conclusions:

• AntigenBoost is an effective bioinformatics tool for optimizing mRNA antigen expression.
• Amino acid substitutions can overcome translation challenges posed by destabilizing dipeptides.
• The AntigenBoost approach holds significant promise for improving mRNA vaccine design and efficacy.