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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Conservation of Protein Domains02:26

Conservation of Protein Domains

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

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lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests.

Valerio Mariani1, Marco Biasini, Alessandro Barbato

  • 1Biozentrum, Universität Basel, Klingelbergstrasse 50-70 and Computational Structural Biology, SIB Swiss Institute of Bioinformatics, 4056 Basel, Switzerland.

Bioinformatics (Oxford, England)
|August 30, 2013
PubMed
Summary

The Local Distance Difference Test (lDDT) offers a robust, superposition-free method for assessing protein model quality. It accurately evaluates local atomic details and stereochemical plausibility, outperforming traditional global measures.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Assessing protein structure prediction requires objective criteria beyond global superposition methods.
  • Conventional measures are sensitive to domain motions and fail to capture local atomic accuracy.
  • There is a need for robust metrics that evaluate local model quality and stereochemical plausibility.

Purpose of the Study:

  • To introduce and validate the Local Distance Difference Test (lDDT) as a novel metric for protein structure assessment.
  • To demonstrate lDDT's ability to evaluate local model quality independent of global superposition.
  • To establish lDDT as a reliable tool for automated assessment of protein structure prediction servers.

Main Methods:

  • Developed the Local Distance Difference Test (lDDT), a superposition-free scoring method.
  • lDDT evaluates local distance differences across all atoms in a protein model.
  • The method incorporates validation of stereochemical plausibility and can use single structures or ensembles as references.

Main Results:

  • lDDT effectively assesses local protein model quality, even with significant domain movements.
  • The score demonstrates good correlation with traditional global similarity measures.
  • lDDT proves to be a robust tool for automated, intervention-free evaluation of structure prediction servers.

Conclusions:

  • The Local Distance Difference Test (lDDT) provides a superior method for evaluating protein model accuracy at a local level.
  • lDDT overcomes limitations of global superposition methods, offering more reliable assessments.
  • This metric enhances the automated evaluation of protein structure prediction tools.