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Related Experiment Videos

A segment alignment approach to protein comparison.

Yuzhen Ye1, Lukasz Jaroszewski, Weizhong Li

  • 1The Burnham Institute, La Jolla, CA 92037, USA.

Bioinformatics (Oxford, England)
|April 15, 2003
PubMed
Summary
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A new SEgment Alignment (SEA) algorithm compares proteins using predicted local structure segments (PLSSs). SEA improves protein alignment quality, even matching structural alignments for proteins with low sequence similarity.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural bioinformatics

Background:

  • Local structure segments (LSSs) are key protein structural units.
  • Predicted LSSs (PLSSs) aid protein folding simulations.
  • Existing sequence alignment methods rarely use LSSs.

Purpose of the Study:

  • To develop a novel algorithm for protein comparison using PLSSs.
  • To improve protein sequence alignment quality by incorporating structural information.
  • To enable sequence-based alignment methods to achieve structural alignment accuracy.

Main Methods:

  • Developed the SEgment Alignment (SEA) algorithm.
  • Represented proteins as networks of PLSSs.
  • Employed network matching to find optimal protein alignments.

Related Experiment Videos

  • Simultaneously predicted local structures and aligned proteins.

    • Main Results:

    • SEA algorithm effectively compares proteins based on PLSSs.
    • SEA significantly improves alignment quality over FFAS profile-profile alignment.
    • SEA achieved alignments comparable to structural alignments for low-similarity proteins.

    Conclusions:

    • SEA offers a new paradigm for protein sequence comparison.
    • Incorporating predicted local structure information enhances alignment accuracy.
    • SEA overcomes limitations of traditional sequence-based alignment methods.