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Information content of molecular structures.

David C Sullivan1, Tiba Aynechi, Vincent A Voelz

  • 1Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143-2240, USA.

Biophysical Journal
|June 28, 2003
PubMed
Summary
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We developed methods to quantify the information content of macromolecule conformations using Shannon information theory. Experimental uncertainties, or "noise," complicate this measurement, but can be managed using communication theory concepts.

Area of Science:

  • Computational chemistry
  • Statistical mechanics
  • Information theory

Background:

  • Shannon information theory provides a framework for quantifying the information content of conformational ensembles.
  • Measuring information content is crucial for understanding macromolecular structure and dynamics.
  • Experimental data often involves uncertainties, complicating direct information content calculations.

Purpose of the Study:

  • To develop and apply methods for calculating the information content of macromolecular ensembles.
  • To investigate the impact of experimental uncertainties (noise) on information content measurements.
  • To explore the relationship between information content, degrees of freedom, and chain length.

Main Methods:

  • Utilized Shannon information theory for calculating ensemble information content.

Related Experiment Videos

  • Introduced "noisy" constraints to model experimental uncertainties.
  • Employed the "noise sphere" concept from communication theory to quantify information loss due to noise.
  • Performed numerical simulations on two-dimensional lattice polymer ensembles and off-lattice polyalanine chains.

    • Main Results:

    • Information content can be calculated for enumerated conformers and lattice walks.
    • Noise in constraints significantly complicates information content measurement, requiring assumptions on noise distribution and clustering.
    • The "noise sphere" effectively links measurement uncertainty to information loss.
    • Information per degree of freedom largely removes chain length dependence for both lattice and off-lattice models.

    Conclusions:

    • Shannon information theory is applicable to quantifying conformational ensemble information.
    • Modeling experimental noise is essential for realistic information content assessment.
    • The "noise sphere" provides a valuable tool for understanding information loss in the presence of uncertainty.
    • Results suggest a universal behavior of information content per degree of freedom across different polymer models.