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Modeling bond correlations in denatured proteins and polypeptides.

Marcos R Betancourt1

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Numerical simulations reveal that bond-orientational correlations in finite polypeptides and denatured proteins deviate from typical decay patterns. A new model captures these complex correlations, offering insights into polypeptide backbone stiffness.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Polymer Science

Background:

  • Bond-orientational correlations characterize polypeptide backbone stiffness.
  • These correlations typically exhibit exponential or power-law decay in the asymptotic limit.
  • However, finite-length polypeptides and denatured proteins show deviations from these standard decay models.

Purpose of the Study:

  • To investigate bond-orientational correlations in finite-length homopolypeptides and denatured proteins.
  • To develop a model that accurately describes these correlations, considering chain length and side-chain properties.
  • To compare simulation results with experimental findings on persistence lengths.

Main Methods:

  • Numerical simulations using a polypeptide model with a potential of mean force.
  • Development of a heuristic model incorporating power-law and multi-exponential terms.
  • Fitting correlations to single exponentials to determine average persistence lengths.

Main Results:

  • Simulations showed significant deviations from single exponential or power-law decay for finite polypeptides and denatured proteins.
  • The developed heuristic model successfully analyzes homopolypeptide correlations, reducing to power-law behavior asymptotically.
  • For denatured proteins, power-law behavior was suppressed, with multi-exponential terms effectively modeling correlations.
  • Average persistence lengths (2.0–2.5 nm) were obtained and found to agree with experimental results.

Conclusions:

  • Standard decay models are insufficient for describing correlations in finite polypeptides and denatured proteins.
  • The new heuristic model provides a more accurate representation of these complex correlations.
  • Simulation-derived persistence lengths align with experimental measurements, validating the model's utility.