Ligand Binding Data Analysis: Theoretical and Practical Aspects

  • Peter J. Munson
Part of the NATO ASI Series book series (NSSA, volume 72)

Abstract

Popularity of the ligand-receptor binding study as a means of investigating the mechanism of action of drugs, hormones, and neurotransmitters has grown considerably during the last decade. This growth is largely due to the availability of radiolabeled compounds, the simplicity of the method and its versatility of application. The low “energy barrier” of the methodology and the relative ease with which experiments may be performed may sometimes result in comparably small effort being spent on the data analysis. The most commonly used approach for data analysis is the preparation of a Scatchard plot of the B/F ratio vs. Bound ligand concentration. In the simplest case, the data will lie along a straight line from which one may abstract the binding affinity from the slope and the binding capacity from the [Bound] axis intercept. However, the data may sometimes show a curvilinear relationship which complicates the analysis considerably. Even when the Scatchard plot is linear, there are numerous pitfalls in the interpretation of the analysis. Resulting misunderstanding and even abuse of the Scatchard analysis is wide-spread in the literature, to the extent that a prominent physical chemist was recently prompted to write that “in most cases... the conclusions drawn from the Scatchard graph are completely untenable”1. Although this statement may be overly pessimistic, the sentiment is well founded. Ligand binding studies have been and will continue to be an important source of information about the mechanism of receptor action. Yet investigators are well advised to be cautious in the interpretation of their analysis. In this paper we review some of the more common errors in the analysis, and discuss how routine use of a well-designed computerized, model fitting program may aid in avoiding them.

Keywords

Binding Capacity Background Count Scatchard Plot Label Ligand Residual Mean Square 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1984

Authors and Affiliations

  • Peter J. Munson
    • 1
  1. 1.Laboratory of Theoretical and Physical BiologyNational Institute of Child Health and Human Development NIHBethesdaUSA

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