Probing receptor-ligand interactions by sedimentation equilibrium

John S. Philo

doi:10.1117/12.274348

22 May 1997 Probing receptor-ligand interactions by sedimentation equilibrium

John S. Philo

Proceedings Volume 2985, Ultrasensitive Biochemical Diagnostics II; (1997) https://doi.org/10.1117/12.274348
Event: BiOS '97, Part of Photonics West, 1997, San Jose, CA, United States

Abstract

While sedimentation equilibrium is most commonly used to characterize the molecular weight and state of association of single proteins, this technique is also a very powerful tool for probing the interactions between two or more different proteins, and can characterize both the binding stoichiometry and the equilibrium constants. To resolve the complex binding interactions that can occur in such systems, it is crucial to globally fit data from many experiments to a common binding model, including samples made with different mixing ratios and a wide range of total concentration. It is often also essential to constrain the parameters during fitting so that the fits correctly reproduce the molar ratio of proteins used in making each sample. We have applied this methodology to probe mechanisms of receptor activation for a number of hematopoietic receptors and their cognate ligands, using receptor extracellular domains expressed as soluble proteins. Such data can potentially help in the design of improved or new protein therapeutics, as well as in efforts to create small- molecular mimetics of protein hormones through structure- based drug design. Sedimentation equilibrium has shown that stem cell factor, erythropoietin, and granulocyte-colony stimulating factor can each dimerize their respective receptors in solution, but the mechanism of ligand-induced receptor dimerization for these three systems are strikingly different.

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John S. Philo "Probing receptor-ligand interactions by sedimentation equilibrium", Proc. SPIE 2985, Ultrasensitive Biochemical Diagnostics II, (22 May 1997); https://doi.org/10.1117/12.274348

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KEYWORDS

Receptors

Proteins

Data modeling

Molecules

Absorbance

Stem cells

Statistical analysis

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