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Related Experiment Videos

Increased detection of structural templates using alignments of designed sequences.

Stefan M Larson1, Amit Garg, John R Desjarlais

  • 1Department of Chemistry and Biophysics Program, Stanford University, California 94305-5080, USA.

Proteins
|April 16, 2003
PubMed
Summary

Computationally designed protein sequences improve protein structure prediction by identifying structural templates. This method enhances template discovery beyond naturally occurring sequences, aiding comparative modeling.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein design

Background:

  • Comparative modeling for protein structure prediction relies on multiple sequence alignments for accurate template identification.
  • The utility of natural sequence homologues is limited to proteins with sufficient evolutionary relatives.

Purpose of the Study:

  • To investigate the efficacy of using computationally designed protein sequences for identifying structural templates in comparative modeling.
  • To assess the performance of "reverse" BLAST searches using designed sequence profiles.

Main Methods:

  • Generation of 500 diverse, non-native protein sequences for 264 protein structures using an all-atom protein design algorithm.
  • Application of PSI-BLAST searches on profiles from designed sequences ("reverse" BLAST) to identify structural homologues.

Related Experiment Videos

  • Scanning 49 genomes to evaluate the discovery of novel structural templates.

    • Main Results:

    • "Reverse" BLAST searches achieved near-perfect accuracy in identifying true structural homologues, with 54% coverage.
    • Novel structural templates, not found by standard PSI-BLAST against the Protein Data Bank (PDB), were identified in 41 out of 49 scanned genomes.
    • The method demonstrated significant potential for identifying distantly related structural templates.

    Conclusions:

    • Computationally designed protein sequence alignments offer benefits comparable to natural sequences for template identification in comparative modeling.
    • This approach expands the scope of template discovery, particularly for proteins lacking natural homologues.
    • Further optimization holds promise for a robust strategy in identifying distant structural relationships.