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Related Experiment Videos

SATCHMO: sequence alignment and tree construction using hidden Markov models.

Robert C Edgar1, Kimmen Sjölander

  • 1195 Roque Moraes Drive, Mill Valley, CA 94941, USA. bob@drive5.com

Bioinformatics (Oxford, England)
|July 23, 2003
PubMed
Summary
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A new algorithm, SATCHMO, improves multiple protein sequence alignment by predicting structurally alignable regions. This method enhances domain identification and has implications for understanding biological mechanisms.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Multiple protein sequence alignment is challenging with low sequence identity or structural divergence.
  • Existing methods like ClustalW and profile HMMs have limitations in handling diverse protein families.
  • SATCHMO offers a novel approach to address these alignment challenges.

Purpose of the Study:

  • To introduce SATCHMO, a new algorithm for multiple protein sequence alignment.
  • To develop a method that simultaneously builds a tree and multiple sequence alignments.
  • To predict structurally alignable regions within protein subgroups.

Main Methods:

  • SATCHMO constructs a tree and multiple sequence alignments concurrently for each internal node.

Related Experiment Videos

  • It generates profile hidden Markov models (HMMs) at each node for alignment and branching order determination.
  • The algorithm predicts alignable and non-alignable positions within subgroups.

    • Main Results:

    • SATCHMO demonstrates performance comparable to ClustalW and UCSC SAM HMM on the BAliBASE benchmark.
    • The algorithm was successfully applied to identify protein domains in potassium channels.
    • Findings have implications for understanding tumor necrosis factor alpha's effect on potassium current.

    Conclusions:

    • SATCHMO provides an effective solution for multiple protein sequence alignment, particularly with low sequence identity.
    • The method's ability to predict structurally alignable regions enhances protein domain identification.
    • This advancement has potential applications in various areas of biological research.