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Related Experiment Videos

Database search based on Bayesian alignment.

J Zhu1, R Lüthy, C E Lawrence

  • 1Department of Computational Biology, Amgen, Inc., Thousand Oaks, CA 91320, USA. junz@amgen.com

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|April 29, 2000
PubMed
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Bayesian search effectively links distantly related protein sequences, outperforming traditional methods. This advance aids in predicting protein structures and functions for large sequence databases.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Protein sequence databases are rapidly expanding, posing challenges for structure and function prediction.
  • Identifying relationships between distantly related protein sequences often requires intermediate models or sequences.

Purpose of the Study:

  • To introduce and evaluate a novel model-based method, Bayesian search, for connecting distantly related protein sequences.
  • To compare the performance of Bayesian search against established sequence comparison methods.

Main Methods:

  • Development of a Bayesian search model for inferring relationships between protein sequences.
  • Comparative analysis using structural databases (SCOP PDB40D-B) with structural similarity as the relationship criterion.

Related Experiment Videos

  • Evaluation against pairwise (Smith-Waterman) and multiple sequence comparison (PSI-Blast) methods.

    • Main Results:

    • Bayesian search consistently linked more distantly related sequence pairs across large protein families compared to other methods.
    • On the SCOP database, Bayesian search identified 36.5% of related pairs with one error per query, surpassing PSI-Blast (32.6%) and Smith-Waterman (25%).
    • Predicted alignments from Bayesian search showed strong agreement with structural alignments, with analysis of false positives at low cutoff values.

    Conclusions:

    • Bayesian search offers a robust and effective approach for identifying remote homology in protein sequences.
    • This method enhances the ability to predict protein structures and functions, particularly for sequences lacking direct similarity.
    • The findings suggest Bayesian search as a valuable tool for navigating large-scale protein sequence data.