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Related Experiment Videos

Benchmarking template selection and model quality assessment for high-resolution comparative modeling.

M I Sadowski1, D T Jones

  • 1Bioinformatics Unit, Department of Computer Science, University College London, London WC1E 6BT, United Kingdom.

Proteins
|July 12, 2007
PubMed
Summary
This summary is machine-generated.

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Selecting the right protein template is crucial for accurate structure prediction. While BLAST is effective in most cases, advanced methods like HHpred and model quality assessment improve results for low-sequence similarity targets.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein structure prediction

Background:

  • Comparative modeling is the leading method for protein structure prediction.
  • Template selection significantly impacts the accuracy of predicted protein models.
  • Identifying optimal template selection strategies is essential for improving modeling outcomes.

Purpose of the Study:

  • To compare various template selection methods for protein structure prediction.
  • To evaluate the utility of sequence similarity versus structurally defined subsets for template selection.
  • To assess the impact of combining model quality assessment with sequence similarity for enhanced model selection.

Main Methods:

  • Utilized a dataset of 732 protein targets with 10+ templates (30-80% sequence identity).

Related Experiment Videos

  • Compared template selection methods: BLAST, PSI-BLAST, profile-profile alignment, HHpred, global sequence alignment, and MQAP.
  • Investigated the predictive power of structurally defined sequence subsets against overall sequence similarity.

    • Main Results:

    • BLAST successfully selected appropriate templates in 75% of cases.
    • No significant improvement was observed with sophisticated sequence-based methods at high sequence identities.
    • HHpred and MQAP enhanced template ranking for targets with 35-40% maximum sequence identity.
    • Structurally defined subsets were generally less discriminative than overall sequence similarity.

    Conclusions:

    • BLAST is a reliable method for template selection, but improvements are possible.
    • Advanced methods like HHpred and MQAP offer benefits for low-sequence similarity targets.
    • Integrating model quality assessment with sequence similarity boosts the identification of optimal protein models by an additional 7%.