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The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement.

Arron Bale1, Robert Rambo2, Christopher Prior1

  • 1Department of Mathematical Sciences, Durham University, Durham, United Kingdom.

Plos Computational Biology
|November 27, 2023
PubMed
Summary
This summary is machine-generated.

We developed a novel method to measure protein entanglement, aiding flexible structure comparison. This approach uses a smoothing algorithm and topological measures to analyze protein folding and predict structures.

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Area of Science:

  • Structural biology
  • Computational biology
  • Biophysics

Background:

  • Protein tertiary structure comparison is challenging, especially for flexible proteins.
  • Existing methods may not adequately capture the complexities of protein folding and entanglement.
  • Topological measures have shown promise in analyzing biomolecular structures.

Purpose of the Study:

  • To develop and present fast, simple measures for protein tertiary structure entanglement.
  • To enable highly flexible structure comparison.
  • To derive empirical bounds on protein entanglement and its relation to secondary structure elements.

Main Methods:

  • Utilized the SKMT algorithm for smoothing the Cα backbone into a minimal complexity curve representation.
  • Applied measures based on writhe and crossing number, common in DNA topology.
  • Derived empirical bounds on protein entanglement relative to the number of secondary structure elements.

Main Results:

  • Identified large-scale helical geometries as dominant contributors to protein monomer entanglement growth.
  • Demonstrated the prevalence of this helical geometry across diverse protein types and sequences.
  • Showed that entanglement bounds can constrain protein structure prediction from small angle X-ray scattering data.

Conclusions:

  • The SKMT-based entanglement measures are effective for flexible protein structure comparison.
  • Entanglement analysis provides insights into protein folding, particularly the role of helical structures.
  • The developed methods offer a valuable tool for protein structure prediction and comparison, especially for proteins in solution.