Stochastic modeling of antibody binding predicts programmable migration on antigen patterns.

Area of Science:

• Biophysics
• Nanotechnology
• Immunology

Background:

• Pathogen surfaces often display repeating molecules that interact with host immune responses.
• The behavior of immune molecules with multiple binding sites on patterned surfaces is not well understood.

Purpose of the Study:

• To develop a computational framework for modeling antibody interactions with patterned antigen substrates.
• To investigate how antigen spacing influences antibody behavior and movement.

Main Methods:

• Utilized a pipeline for constructing mechanistic models of antibody-antigen interactions.
• Employed DNA origami nanostructures for precise spatial arrangement of antigens.
• Simulated antibody dynamics on substrates with varying antigen densities and patterns.

Main Results:

• Antigen spacing was identified as a critical parameter controlling antibody residence time and migration speed.
• Model predictions indicated that gradients in antigen spacing can induce directed antibody migration.
• Antibody-antigen interactions were characterized as a computational system influenced by antigen geometry.

Conclusions:

• Precise control over antigen arrangement can direct molecular movement, akin to a computational system.
• This molecular programmability has implications for understanding pathogen-host co-evolution.
• The findings suggest potential applications in designing novel molecular machines.