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Genome analysis: Assigning protein coding regions to three-dimensional structures.

A A Salamov1, M Suwa, C A Orengo

  • 1Helix Research Institute, Chiba, Japan.

Protein Science : a Publication of the Protein Society
|April 22, 1999
PubMed
Summary
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This study presents a novel sequence alignment method to identify more protein structure relationships. The approach enhances protein structure prediction and functional assignment across multiple genomes.

Area of Science:

  • Bioinformatics
  • Structural Biology
  • Genomics

Background:

  • Linking protein sequences to known 3D structures is crucial for understanding protein function.
  • Existing methods often rely on sensitive fold recognition software, which can be computationally intensive or have limitations.

Purpose of the Study:

  • To develop and evaluate a procedure for maximizing the number of protein sequences reliably linked to known 3D structures.
  • To improve the detection of relationships between protein sequences and structures using conventional alignment tools.

Main Methods:

  • Utilized conventional sequence alignment tools, optimizing their application for increased relationship detection.
  • Analyzed 11 genomes, assessing Open Reading Frames (ORFs) for significant matches to proteins with known structures.

Related Experiment Videos

  • Defined criteria for significant alignment (e.g., >100 residues or >50% of the smaller sequence).

    • Main Results:

    • Identified significant matches for 23-32% of ORFs across analyzed genomes.
    • Achieved higher detection rates compared to many existing methods, including those with fold-recognition components.
    • Found that aligned regions met stringent length or coverage criteria.

    Conclusions:

    • The optimized sequence alignment procedure effectively increases the number of detected protein sequence-structure relationships.
    • This method offers a sensitive and efficient alternative for structural and functional annotation of gene products.
    • Further analysis of biochemical and structural characteristics can aid in realistic functional assignment.