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Related Experiment Videos

Confidence measures for protein fold recognition.

Ingolf Sommer1, Alexander Zien, Niklas von Ohsen

  • 1Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. ingolf.sommer@gmd.de

Bioinformatics (Oxford, England)
|June 21, 2002
PubMed
Summary
This summary is machine-generated.

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We evaluated protein homology detection methods, finding profile-based threading superior. Score gap functions effectively assess confidence for methods lacking theoretical p-values, improving remote homolog identification.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Detecting remote protein homologs is crucial for understanding protein function and evolution.
  • Existing methods for remote homology detection vary in sensitivity and reliability.
  • Assessing the confidence of predicted homologs is essential for accurate biological interpretation.

Purpose of the Study:

  • To comprehensively evaluate methods for detecting remote protein homologs.
  • To develop sensitive search strategies for identifying potential protein candidates.
  • To establish reliable confidence measures for selecting the most accurate predictions.

Main Methods:

  • Systematic comparison of sequence alignment and threading methods, with and without sequence profiles.

Related Experiment Videos

  • Evaluation of various confidence measures including raw scores, z-scores, and p-value estimations.
  • Development and application of empirical approximations for p-values where theoretical distributions are absent.

    • Main Results:

    • Threading methods incorporating sequence profiles outperform traditional sequence alignment.
    • For local alignments, p-value methods are optimal, with score gaps offering comparable performance.
    • Score gap functions provide effective confidence measures for global and threading methods lacking theoretical p-values.

    Conclusions:

    • Profile-based threading methods represent the state-of-the-art for remote protein homology detection.
    • Score gap functions offer a practical and effective approach to confidence estimation in homology searches.
    • This study provides a robust framework for evaluating and improving protein remote homolog detection strategies.