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SAD--a normalized structural alignment database: improving sequence-structure alignments.

Brian Marsden1, Ruben Abagyan

  • 1The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA. marsden@scripps.edu

Bioinformatics (Oxford, England)
|April 17, 2004
PubMed
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A new structural alignment database aids in optimizing sequence-structure alignment algorithms for homology modeling. This resource ensures reliable alignments from diverse sources, improving protein structure prediction accuracy.

Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein structure analysis

Background:

  • Development of sequence-structure alignment algorithms is crucial for homology modeling.
  • Existing databases may lack comprehensive coverage or reliable alignments.
  • High-resolution structural data is essential for accurate alignment validation.

Purpose of the Study:

  • To create a comprehensive structural alignment database for optimizing alignment algorithms.
  • To ensure thorough sampling of protein fold-space.
  • To provide accurate and reliable structural alignments for computational biology applications.

Main Methods:

  • Integrated alignments from HOMSTRAD, BAliBASE, and Gerstein databases.
  • Developed a novel Contact Alignment Quality score for alignment evaluation.

Related Experiment Videos

  • Generated alignments using a described global structural alignment method.

    • Main Results:

    • Compiled a non-redundant, unbiased database of 1927 structural alignments.
    • Included high-resolution structures (better than 2.5 Å) across diverse fold-space.
    • Demonstrated the utility of the Contact Alignment Quality score for discriminating alignment quality.

    Conclusions:

    • The database provides a valuable resource for developing and refining sequence-structure alignment algorithms.
    • The novel scoring function enhances the reliability of structural alignment assessments.
    • This resource facilitates advancements in homology modeling and protein structure prediction.