A multivalent binding model infers antibody Fc species from systems serology

Area of Science:

• Immunology
• Structural Biology
• Computational Biology

Background:

• Systems serology profiles antibody functions but lacks structural insights.
• Understanding Fc structure-antibody interactions is crucial for disease research and therapy design.

Purpose of the Study:

• To develop a modeling approach to infer antibody Fc species from systems serology data.
• To identify specific antibody structural features driving immune responses.
• To uncover new insights into antibody fucosylation and its role in disease.

Main Methods:

• Developed a computational model for molecular interactions in systems serology assays.
• Validated the model's accuracy in inferring antibody properties.
• Applied the model to existing systems serology data to determine antibody fucosylation levels.

Main Results:

• COVID-19 vaccine efficacy correlates with afucosylated spike protein-targeting IgG.
• Afucosylated IgG is associated with membrane-associated antigens for COVID-19 and HIV, specifically on host cell membranes.
• Challenged the assumption of lower fucosylation in HIV controllers' gp120-targeting IgG.
• Identified redundant and information-rich measurements within systems serology assays.

Conclusions:

• The modeling approach provides a quantitative framework for systems serology data analysis.
• Enables improved mechanistic conclusions and the design of targeted antibody therapies.
• Highlights the significance of antibody fucosylation in vaccine efficacy and immune responses.