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Protein Organization01:13

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Protein Folding01:22

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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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

PFRES: protein fold classification by using evolutionary information and predicted secondary structure.

Ke Chen1, Lukasz Kurgan

  • 1Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.

Bioinformatics (Oxford, England)
|October 19, 2007
PubMed
Summary

A new method, PFRES, accurately classifies protein folds from low-sequence-identity proteins. This computational approach improves tertiary structure prediction by identifying structural similarity without relying solely on sequence similarity.

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Last Updated: Jun 29, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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Published on: November 3, 2011

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein science

Background:

  • The discovery of novel protein structures and SCOP categories is slowing, suggesting the protein structure space is nearing completion.
  • Current tertiary structure prediction methods are less effective for proteins lacking homologous templates.
  • Identifying structural similarity independent of sequence similarity is crucial for improving protein structure prediction.

Purpose of the Study:

  • To develop an automated method for protein fold classification using low-sequence-identity (<35%) protein sequences.
  • To enhance tertiary structure prediction by determining structural similarity without relying on sequence similarity.

Main Methods:

  • Developed the PFRES method for automated protein fold classification.
  • Utilized a novel, compact feature representation with significantly fewer features (36 vs. 283) compared to existing methods.
  • Combined evolutionary information (PSI-BLAST profile-based composition vector) with predicted secondary structure information (PSI-PRED).
  • Employed a carefully designed, ensemble-based classifier.

Main Results:

  • PFRES achieved 66.4% and 68.4% accuracy on two independent test sets for protein fold classification.
  • PFRES demonstrated 6.3-12.4% higher accuracy compared to existing methods.
  • The prediction accuracy of PFRES was statistically significantly better than competing methods.
  • The method uses a highly efficient feature representation, reducing feature count by nearly 90%.

Conclusions:

  • The PFRES method offers a significant improvement in automated protein fold classification, particularly for low-sequence-identity proteins.
  • This approach enhances tertiary structure prediction by effectively identifying structural similarities even with minimal sequence homology.
  • The method's efficiency and improved accuracy provide a valuable tool for structural bioinformatics and protein science research.