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Related Experiment Videos

Forward rate constants for receptor clusters. Variational methods for upper and lower bounds.

J H van Opheusden, F W Wiegel, B Goldstein

    Biophysical Chemistry
    |October 1, 1984
    PubMed
    Summary

    Receptor clustering significantly reduces the ligand-binding rate constant (k+). This study provides bounds for this reduction in various cluster configurations, aiding in understanding cell surface receptor dynamics.

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    Area of Science:

    • Biophysics
    • Chemical Kinetics
    • Computational Biology

    Background:

    • Cell surface receptors play crucial roles in cellular communication.
    • Ligand-receptor binding kinetics are fundamental to understanding biological processes.
    • The spatial arrangement of receptors can influence binding efficiency.

    Purpose of the Study:

    • To quantify the effect of receptor clustering on the diffusion-limited forward rate constant (k+) for ligand-receptor binding.
    • To estimate the reduction in k+ for various receptor cluster configurations.
    • To develop theoretical bounds for ligand flux into clustered receptors.

    Main Methods:

    • Derivation of two alternative expressions for ligand flux into surface-distributed receptors.
    • Application of a variational principle to establish upper and lower bounds on flux.

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  • Utilizing an electrostatics analogy for calculating rigorous bounds.
  • Exact calculation of flux into a spheroidal receptor.

    • Main Results:

    • The study provides rigorous bounds on the reduction of k+ due to receptor clustering.
    • Bounds were calculated to be within approximately 10% of the exact result for planar clusters.
    • An exact solution for flux into a spheroidal receptor was obtained and used for cluster analysis.

    Conclusions:

    • Receptor clustering demonstrably reduces the effective ligand-binding rate.
    • The developed theoretical framework provides accurate estimations for flux reduction in clustered receptor systems.
    • These findings are critical for modeling cell signaling and drug-receptor interactions.