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Computing phenomenologic Adair-Klotz constants from microscopic MWC parameters.

Melanie I Stefan1, Stuart J Edelstein, Nicolas Le Novère

  • 1Computational Neurobiology Group, EMBL-EBI, Wellcome-Trust Genome Campus, Hinxton, UK. mstefan@ebi.ac.uk

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Computational models of allosteric proteins are hard to validate. This study provides a method to calculate apparent Adair-Klotz constants from microscopic parameters of generalized concerted models, aiding model validation.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Biochemistry

Background:

  • The Monod-Wyman-Changeux (MWC) allosteric framework is widely used but difficult to validate experimentally.
  • Experimental data often uses the Adair-Klotz approach, which does not directly measure microscopic constants.
  • A gap exists between theoretical allosteric models and experimental data interpretation.

Purpose of the Study:

  • To develop a method for computing apparent Adair-Klotz constants from microscopic parameters of generalized allosteric models.
  • To bridge the gap between theoretical MWC models and experimental Adair-Klotz descriptions.
  • To facilitate the validation of computational models in allosteric research.

Main Methods:

  • Developed a generalized concerted model with two states (R and T) and non-equivalent binding sites.
  • Derived equations to compute apparent Adair-Klotz constants from microscopic association constants and allosteric parameters.
  • Applied the framework to existing models of calmodulin and hemoglobin.

Main Results:

  • Successfully computed apparent Adair-Klotz constants from microscopic parameters for a generalized allosteric model.
  • Demonstrated the framework's applicability to calmodulin and hemoglobin, representing diverse allosteric behaviors.
  • Provided a quantitative link between theoretical MWC parameters and experimental Adair-Klotz constants.

Conclusions:

  • The proposed method enables the validation of generalized MWC allosteric models by relating theoretical parameters to experimental observables.
  • This approach facilitates direct comparison between computational predictions and experimental data, improving model accuracy.
  • The framework offers a valuable tool for researchers studying allosteric systems like calmodulin and hemoglobin.