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The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement
by
Prior, Christopher
, Rambo, Robert
, Bale, Arron
in
Algorithms
/ Amino acids
/ Analysis
/ Complexity
/ Crystal structure
/ Crystals
/ Design
/ Entanglement
/ Flexible structures
/ Machine learning
/ Methods
/ Protein folding
/ Protein structure
/ Protein structure prediction
/ Protein Structure, Secondary
/ Proteins
/ Proteins - chemistry
/ Secondary structure
/ Sequences
/ Smoothing
/ Structure
/ Tertiary structure
/ Topology
/ X-ray scattering
2023
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The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement
by
Prior, Christopher
, Rambo, Robert
, Bale, Arron
in
Algorithms
/ Amino acids
/ Analysis
/ Complexity
/ Crystal structure
/ Crystals
/ Design
/ Entanglement
/ Flexible structures
/ Machine learning
/ Methods
/ Protein folding
/ Protein structure
/ Protein structure prediction
/ Protein Structure, Secondary
/ Proteins
/ Proteins - chemistry
/ Secondary structure
/ Sequences
/ Smoothing
/ Structure
/ Tertiary structure
/ Topology
/ X-ray scattering
2023
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Do you wish to request the book?
The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement
by
Prior, Christopher
, Rambo, Robert
, Bale, Arron
in
Algorithms
/ Amino acids
/ Analysis
/ Complexity
/ Crystal structure
/ Crystals
/ Design
/ Entanglement
/ Flexible structures
/ Machine learning
/ Methods
/ Protein folding
/ Protein structure
/ Protein structure prediction
/ Protein Structure, Secondary
/ Proteins
/ Proteins - chemistry
/ Secondary structure
/ Sequences
/ Smoothing
/ Structure
/ Tertiary structure
/ Topology
/ X-ray scattering
2023
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The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement
Journal Article
The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement
2023
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Overview
We present fast and simple-to-implement measures of the entanglement of protein tertiary structures which are appropriate for highly flexible structure comparison. These are performed using the SKMT algorithm, a novel method of smoothing the C α backbone to achieve a minimal complexity curve representation of the manner in which the protein’s secondary structure elements fold to form its tertiary structure. Its subsequent complexity is characterised using measures based on the writhe and crossing number quantities heavily utilised in DNA topology studies, and which have shown promising results when applied to proteins recently. The SKMT smoothing is used to derive empirical bounds on a protein’s entanglement relative to its number of secondary structure elements. We show that large scale helical geometries dominantly account for the maximum growth in entanglement of protein monomers, and further that this large scale helical geometry is present in a large array of proteins, consistent across a number of different protein structure types and sequences. We also show how these bounds can be used to constrain the search space of protein structure prediction from small angle x-ray scattering experiments, a method highly suited to determining the likely structure of proteins in solution where crystal structure or machine learning based predictions often fail to match experimental data. Finally we develop a structural comparison metric based on the SKMT smoothing which is used in one specific case to demonstrate significant structural similarity between Rossmann fold and TIM Barrel proteins, a link which is potentially significant as attempts to engineer the latter have in the past produced the former. We provide the SWRITHE interactive python notebook to calculate these metrics.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
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