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Related Experiment Videos

TM-align: a protein structure alignment algorithm based on the TM-score.

Yang Zhang1, Jeffrey Skolnick

  • 1Center of Excellence in Bioinformatics, University at Buffalo 901 Washington Street, Buffalo, NY 14203, USA.

Nucleic Acids Research
|April 26, 2005
PubMed
Summary
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We developed TM-align, a fast and accurate protein structure alignment algorithm. It improves upon existing methods, enabling efficient identification of protein folds and assessment of predicted structures.

Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein structure analysis

Background:

  • Protein structure alignment is crucial for understanding protein function and evolution.
  • Existing methods like CE, DALI, and SAL have limitations in speed and accuracy.
  • Accurate structural comparisons are essential for evaluating protein structure prediction models.

Purpose of the Study:

  • To introduce TM-align, a novel algorithm for protein structure alignment.
  • To compare TM-align's performance against established methods in terms of speed and accuracy.
  • To assess TM-align's utility in analyzing large protein databases and evaluating protein structure predictions.

Main Methods:

  • Developed TM-align, combining TM-score rotation matrix and Dynamic Programming (DP).

Related Experiment Videos

  • Performed all-against-all structure comparison of 10,515 representative protein chains from the Protein Data Bank (PDB).
  • Applied TM-align to match TASSER-predicted models against solved proteins in PDB.

    • Main Results:

    • TM-align is significantly faster (4-20x) than CE, DALI, and SAL.
    • Achieved higher accuracy and coverage in structure alignments compared to existing methods.
    • Identified 1996 distinct protein folds from 10,515 PDB chains using a TM-score threshold of 0.5.
    • TM-align successfully identified close structural analogs for both folded and misfolded models with high coverage.
    • A significant correlation was found between predicted structure correctness and structural similarity to known proteins.

    Conclusions:

    • TM-align offers a substantial improvement in speed and accuracy for protein structure alignment.
    • The algorithm is effective for large-scale fold identification and structural database analysis.
    • TM-align can aid in selecting accurate models from protein structure prediction methods.