In silico design of immunogenic antigen cocktail via affinity maturation-guided optimization

Area of Science:

• Computational biology
• Vaccinology
• Bioinformatics

Background:

• Emerging infectious diseases necessitate proactive vaccine design strategies.
• Traditional vaccine development relies on intuition and experimentation, which is insufficient for rapidly evolving viruses.
• Data-driven, in silico approaches are crucial for efficient vaccine design.

Purpose of the Study:

• To propose a computational pipeline for designing optimized immunogenic vaccine cocktails.
• To shift vaccine design towards data-driven strategies using in silico methods.
• To enhance immune response against new virus strains.

Main Methods:

• A two-stage computational pipeline was developed for antigen identification and selection.
• Predictive models trained on deep mutational scanning data were used for candidate selection based on binding affinity, antibody escape, and sequence diversity.
• A combinatorial optimization framework and sequence-based affinity maturation modeling were employed for optimal cocktail design.

Main Results:

• The pipeline successfully identified and combined antigen candidates to maximize expected immunogenicity.
• Structure-based affinity maturation simulations validated the efficacy of the designed immunogenic cocktails.
• The modular framework demonstrated effectiveness in optimizing vaccine cocktail design.

Conclusions:

• The proposed computational pipeline offers a proactive, data-driven approach to vaccine design.
• This strategy is effective in creating optimized immunogenic cocktails against evolving viral threats.
• The framework facilitates the development of next-generation vaccines with enhanced efficacy.