Topolow: a mapping algorithm for antigenic cross-reactivity and binding affinity assays.

Area of Science:

• Virology and Immunology
• Computational Biology
• Bioinformatics

Background:

• Tracking pathogen evolution, like influenza, dengue, and HIV, is vital for updating vaccines.
• Existing antigenic cartography methods (e.g., multidimensional scaling) struggle with incomplete and complex experimental data, leading to inaccurate evolutionary maps.
• A significant need exists for robust computational tools to map antigenic relationships from sparse data, ensuring biological relevance for vaccine design.

Purpose of the Study:

• To introduce Topolow, a novel algorithm for accurate antigenic mapping from cross-reactivity and binding affinity data.
• To address the limitations of current methods in handling sparse and complex antigenic datasets.
• To provide a stable and biologically relevant framework for understanding pathogen evolution and informing vaccine strategies.

Main Methods:

• Developed Topolow, a physics-inspired algorithm to transform experimental measurements into a phenotype space.
• Employed likelihood-based estimation for optimal dimensionality determination, mitigating distortions from insufficient dimensions.
• Introduced antigenic velocity vectors to quantify the rate and direction of antigenic change over time.

Main Results:

• Topolow achieved comparable or superior prediction accuracy compared to multidimensional scaling for H3N2 influenza, dengue, and HIV.
• The algorithm demonstrated significantly improved accuracy (56% for dengue, 41% for HIV) and maintained complete antigen positioning.
• Topolow exhibited superior stability across multiple runs and effectively reduced experimental noise and bias.

Conclusions:

• Topolow offers a robust and accurate computational approach for antigenic cartography, even with highly incomplete data.
• The algorithm's ability to map antigenic relationships and measure antigenic velocity provides critical insights into pathogen evolution.
• This method holds significant potential for improving the design and efficacy of vaccines against rapidly evolving viruses.