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Dynamic sequence databank searching with templates and multiple alignment

W R Taylor1

  • 1Division of Mathematical Biology, National Institute for Medical Research, The Ridgeway, London, NW7 1AA, UK.

Journal of Molecular Biology
|July 17, 1998
PubMed
Summary
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This study introduces a stable, automated method for iterative sequence database searching. The approach enhances the detection of distantly related protein family members, improving upon existing automated methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Iterative sequence database searching is crucial for identifying protein families.
  • Automating this process is challenging due to instability and error propagation in alignments.
  • Expert supervision is often required to ensure accuracy.

Purpose of the Study:

  • To develop a stable, automated method for iterative sequence database searching.
  • To improve the detection of distantly related protein family members.
  • To apply the method to analyze hormone-binding domains of insulin and growth-factor receptors.

Main Methods:

  • A hybrid approach combining pattern matching (with a BLAST-like pre-filter) and multiple sequence alignment (MULTAL).
  • Sequences are aligned to a fixed similarity limit, and non-family members are rejected.

Related Experiment Videos

  • The core family is refined iteratively by lowering similarity cutoffs.

    • Main Results:

    • The developed method demonstrates stability against misalignments.
    • It achieves performance comparable to Psi-BLAST in recognizing distantly related sequences.
    • The method was successfully applied to analyze hormone-binding domains.

    Conclusions:

    • The novel method provides a stable and automated solution for iterative sequence searching.
    • It effectively extends the detection of protein family members to greater evolutionary distances.
    • This approach enhances the analysis of protein domains and families in bioinformatics.