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Related Experiment Video

Updated: Jun 12, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Exploring the potential of template-based modelling.

Braddon K Lance1, Charlotte M Deane, Graham R Wood

  • 1Department of Statistics, Macquarie University, North Ryde, Australia. braddon.lance@mq.edu.au

Bioinformatics (Oxford, England)
|June 8, 2010
PubMed
Summary
This summary is machine-generated.

Optimally positioning conserved protein fragments from a template structure can significantly improve protein structure prediction accuracy. This method offers substantial gains, particularly for templates with specific characteristics like lower sequence identity.

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Last Updated: Jun 12, 2026

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Structural biology
  • Computational biology
  • Protein structure prediction

Background:

  • Template-based modeling approximates protein structures using homologous templates.
  • Conserved structural regions between template and target form the core of predictions.
  • Improving target prediction accuracy is crucial for understanding protein function.

Purpose of the Study:

  • Quantify potential improvements in protein structure prediction via rigid fragment repositioning.
  • Relate achievable improvements to target, template, and alignment properties.
  • Identify templates amenable to significant accuracy enhancement.

Main Methods:

  • Calculated accuracy improvements using structure pairs from HOMSTRAD, CASP7, and CASP8.
  • Analyzed improvements based on root mean squared deviation (RMSD) and GDT_HA metrics.
  • Employed a generalized linear model to predict improvement extent based on template and alignment features.

Main Results:

  • Optimal rigid fragment positioning commonly yielded 0.7 Å RMSD and 6% GDT_HA improvements.
  • Improvement potential is predictable using four key variables.
  • Templates with more fragments, shorter fragments, higher helical content, and lower sequence identity showed greater improvement scope.
  • Potential for enhanced loop modeling through optimal fragment positioning.

Conclusions:

  • Substantial accuracy improvements in protein structure prediction are achievable by optimally positioning conserved template fragments.
  • This study provides a framework for identifying templates where fragment repositioning can yield significant benefits.
  • Findings guide the selection of templates for targeted structural refinement.