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Related Experiment Videos

Fold recognition from sequence comparisons.

K K Koretke1, R B Russell, A N Lupas

  • 1Protein Bioinformatics Group, GlaxoSmithKline, Collegeville, Pennsylvania 19426-0989, USA. Kristin.K.Koretke@gsk.com

Proteins
|February 9, 2002
PubMed
Summary
This summary is machine-generated.

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A new protocol using PSI-Blast for protein structure prediction achieved 53% accuracy in fold recognition. Manual intervention improved results, correctly identifying folds for 67% of targets.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein structure prediction

Background:

  • Protein structure prediction is crucial for understanding biological function.
  • The Critical Assessment of protein Structure Prediction (CASP) competition benchmarks prediction methods.
  • Fold recognition aims to identify the tertiary structure (fold) of a protein from its amino acid sequence.

Purpose of the Study:

  • To evaluate a novel protocol for protein fold recognition using PSI-Blast.
  • To compare the performance of automated and manually assisted versions of the protocol.

Main Methods:

  • Applied a protocol based on PSI-Blast for fold recognition during CASP4.
  • Utilized a back-validation step to identify significant sequence connections (E-values up to 10).

Related Experiment Videos

  • Employed HMMer for alignment generation when connections to known structures were found.
  • Implemented automated (SBauto) and manual intervention (SBfold) versions.

    • Main Results:

    • The automated version (SBauto) predicted 17 target domains, correctly identifying the fold for 8 (47%).
    • SBauto achieved average alignment accuracies of 24% for residues and 43% for secondary structures.
    • The manual version (SBfold) predicted 15 targets, correctly identifying the fold for 10 (67%).
    • SBfold showed improved alignment accuracies: 33% for residues and 64% for secondary structures.

    Conclusions:

    • The developed protocol shows promise for protein fold recognition.
    • Manual intervention enhances prediction accuracy in fold recognition.
    • Further developments are needed to improve the success rate and accuracy of automated fold recognition.