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Secondary structures in long compact polymers.

Richard Oberdorf1, Allison Ferguson, Jesper L Jacobsen

  • 1Department of Physics, Brandeis University, Waltham, Massachusetts 02454, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 7, 2007
PubMed
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We developed an efficient Monte Carlo algorithm to generate compact polymer configurations. This method allows for longer chain investigations, revealing compact chains are more likely to form secondary structures than open chains.

Area of Science:

  • Statistical mechanics
  • Polymer physics
  • Computational biology

Background:

  • Compact polymers are lattice self-avoiding walks crucial for modeling protein folding.
  • Generating sufficient random configurations for numerical studies is a significant challenge.

Purpose of the Study:

  • To present an efficient Monte Carlo algorithm for uniformly sampling compact polymer configurations.
  • To enable the study of longer polymer chains than previously possible.
  • To investigate secondary structure formation in compact polymers.

Main Methods:

  • Development of a novel Monte Carlo algorithm for uniform sampling of compact polymer configurations.
  • Generation of a large ensemble of polymer configurations for statistical analysis.
  • Computation of secondary structure statistics for varying chain lengths.

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Main Results:

  • The algorithm efficiently generates uniformly sampled compact polymer configurations.
  • The fraction of monomers in secondary structures is self-averaging in the long-chain limit.
  • Compact polymer chains exhibit a significantly higher propensity for secondary structure formation compared to open polymer chains.

Conclusions:

  • The new algorithm overcomes limitations in generating compact polymer configurations.
  • Compact polymers are more prone to forming secondary structures, offering insights into polymer behavior and protein folding.