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Composition gradient static light scattering: A new technique for rapid detection and quantitative characterization of reversible macromolecular hetero-associations in solution

descriptionPublicationkeyboard_double_arrow_right Article 01 Nov 2005Publisher:Elsevier BVJournal:Analytical Biochemistry, volume 346, pages 132-138 (issn: 0003-2697,

Authors: Attri, Arun K.; Minton, Allen P.;

doi: 10.1016/j.ab.2005.08.013

pmid: 16188220

Composition gradient static light scattering: A new technique for rapid detection and quantitative characterization of reversible macromolecular hetero-associations in solution

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Abstract

A method for rapid detection and quantitative characterization of associations between two proteins in solution is presented, based upon continuous measurement of the Rayleigh light scattering and UV absorbance of a solution whose composition is varied with time in a controlled fashion. Collection and subsequent analysis of data are both rapid and semiautomatic. The results of validation experiments demonstrate that the technique can correctly identify complexes and reliably evaluate equilibrium constants for hetero-association.

Related Organizations

National Institutes of Health
United States
National Institute of Diabetes and Digestive and Kidney Diseases
United States
National Institute of Health
Pakistan
Austin Health & Human Services Department
United States

Keywords

Solutions, Light, Chymotrypsin, Fibrinogen, Proteins, Scattering, Radiation, Serum Albumin, Bovine, Trypsin Inhibitors

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	51
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	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%