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Related Experiment Videos

Model and simulation of multivalent binding to fixed ligands

K M Müller1, K M Arndt, A Plückthun

  • 1Biochemisches Institut, Universität Zürich, Winterthurerstrasse 190, Zürich, CH-8057, Switzerland.

Analytical Biochemistry
|August 26, 1998
PubMed
Summary

A new model quantifies multivalent binding, considering local ligand density and analyte concentration. This simulation tool aids in understanding avidity effects and designing high-avidity molecules for biosensors.

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Area of Science:

  • Biochemistry
  • Physical Chemistry
  • Biosensor Technology

Background:

  • Multivalent binding, where a molecule binds to multiple sites, is crucial in biological interactions.
  • Understanding avidity effects is essential for accurate biosensor data interpretation.
  • Existing models may not fully capture the complexities of local density-driven binding kinetics.

Purpose of the Study:

  • To develop a quantitative model for multivalent binding kinetics.
  • To simulate and analyze the influence of local ligand density on binding.
  • To aid in the design and interpretation of biosensor experiments.

Main Methods:

  • Developed a computational model for bivalent binding kinetics.
  • Incorporated local ligand density and analyte concentration into the model.

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  • Simulated binding events in surface plasmon resonance (SPR) biosensors.
  • Compared simulation results with experimental data.

    • Main Results:

    • The model accurately quantifies the impact of bivalent binding on SPR sensor signals.
    • Simulation results demonstrate the influence of ligand density, analyte concentration, and binding site distance.
    • The model successfully captures avidity effects driven by local ligand concentrations.

    Conclusions:

    • The developed model provides a quantitative framework for understanding multivalent binding in biosensors.
    • Simulations are valuable for designing experiments to assess avidity and develop high-avidity molecules.
    • The model helps elucidate the complexity of sensorgram data arising from multivalent interactions.