Network Organization of Antibody Interactions in Sequence and Structure Space: the RADARS Model

Area of Science:

• Immunology
• Systems Biology
• Bioinformatics

Background:

• Adaptive immunity relies on complex molecular interactions within a self-organizing network.
• Interpreting deep sequencing data of immune-receptor repertoires is challenging due to limitations in predicting function from sequence and structure.
• Existing models struggle to fully explain the breadth of antibody-antigen interactions.

Purpose of the Study:

• To propose a novel computational model, RADARS (RAdial ADjustment of System Resolution), for understanding antibody interaction space and network dynamics.
• To provide a framework for interpreting immune-receptor repertoire data by linking sequence, structure, and function.
• To describe the thermodynamic and network properties of adaptive immunity.

Main Methods:

• Development of the RADARS model based on radial growth of antibody interaction affinity in structure space.
• Application of thermodynamic principles to describe antibody-free energy levels as shells.
• Analysis of network properties, including power law distributions and fractal network characteristics.

Main Results:

• The RADARS model describes antibody interactions as a scale-free fractal network with plasma cells as hubs.
• Interaction affinity levels are represented as free energy shells, supporting hierarchical B-cell development.
• The model posits that a finite set of antibody structures can interact with an infinite number of antigens via controlled energy distribution.

Conclusions:

• The RADARS model offers a new perspective on adaptive immunity as a scale-free fractal network.
• Understanding the quantitative network properties of this system can aid in organizing sequence-derived structural data.
• This framework has implications for predicting antibody-antigen interactions and understanding immune system organization.