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

The increasing emergence of new virus strains with increased infectiousness necessitates a more proactive approach for effective vaccine design. To achieve this goal, it is critical to shift the vaccine design paradigm from traditional approaches that rely on expert intuition and experimental methods toward data-driven strategies that leverage in silico design and virtual screening. In this work, we propose a computational pipeline for designing an optimized immunogenic cocktail that can boost the immune response. The proposed pipeline consists of two stages, where potential antigen candidates are identified in the first stage, followed by the optimal selection and combination of the candidates in the second stage to maximize the expected immunogenicity. We leverage predictive models trained using deep mutational scanning data to drive the candidate antigen selection process based on three selection criteria-namely, binding affinity between viral protein and receptor, antibody escape probability, and sequence diversity. To identify the optimal cocktail within the pool of selected antigens, we adopt a combinatorial optimization framework, where the cocktail design is iteratively refined based on the expected efficacy predicted by a sequence-based computational model of affinity maturation. Validation of the designed cocktails through structure-based affinity maturation simulation demonstrates the efficacy of the proposed modular framework for designing an optimized immunogenic cocktail.

The code for cocktail design is available in https://github.com/nafizabeer/Antigen_Cocktail_Design.