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Related Experiment Videos

Two strategies for sequence comparison: profile-preprocessed and secondary structure-induced multiple alignment.

J Heringa1

  • 1Division of Mathematical Biology, National Institute for Medical Research (NIMR), Mill Hill, London, UK. jhering@nimr.mrc.ac.uk

Computers & Chemistry
|July 15, 1999
PubMed
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Computational biology and chemistry·2004

Two novel techniques, profile-preprocessed and secondary structure-induced alignments, enhance dynamic programming for protein sequence analysis. These methods improve sensitivity and consistency in identifying crucial protein regions.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Protein Science

Background:

  • Multiple sequence alignment (MSA) is crucial for understanding protein relationships and identifying key functional/structural regions.
  • Existing dynamic programming methods have limitations in sensitivity and consistency checks for distant protein families.

Purpose of the Study:

  • Introduce two new techniques: profile-preprocessed and secondary structure-induced alignments.
  • Enhance the sensitivity and alignment consistency of dynamic programming for MSA.
  • Evaluate these novel strategies against established methods for distantly related protein sequences.

Main Methods:

  • Developed profile-preprocessed alignments using hierarchical dynamic programming.
  • Developed secondary structure-induced alignments using hierarchical dynamic programming.

Related Experiment Videos

  • Applied both strategies, individually and combined, to flavoxin and cupredoxin protein families.

    • Main Results:

    • The new alignment strategies demonstrated increased sensitivity compared to standard methods.
    • Profile-preprocessed and secondary structure-induced alignments provide improved consistency checks.
    • Comparative analysis showed competitive or superior performance against CLUSTALX and MULTAL for distant sequences.

    Conclusions:

    • Profile-preprocessed and secondary structure-induced alignments are effective enhancements to dynamic programming for MSA.
    • These methods improve the detection of remote homology and functionally important protein regions.
    • The techniques offer valuable alternatives for analyzing challenging, distantly related protein sequence datasets.