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Related Experiment Videos

Hidden Markov models for detecting remote protein homologies

K Karplus1, C Barrett, R Hughey

  • 1Department of Computer Engineering, Jack Baskin School of Engineering, University of California, Santa Cruz, CA 95064, USA.

Bioinformatics (Oxford, England)
|February 3, 1999
PubMed
Summary

The new SAM-T98 hidden Markov model (HMM) method significantly improves protein sequence homology detection. It outperforms existing tools in fold-recognition tests, offering a powerful approach for biological sequence analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Protein sequence analysis is crucial for understanding biological function and evolution.
  • Identifying remote protein homologs is challenging due to sequence divergence.
  • Existing methods like WU-BLASTP and DOUBLE-BLAST have limitations in sensitivity and accuracy.

Purpose of the Study:

  • To introduce and evaluate SAM-T98, a novel hidden Markov model (HMM) method for detecting remote protein sequence homologs.
  • To compare the performance of SAM-T98 against established homology search tools.
  • To demonstrate the utility of SAM-T98 in constructing model libraries from structural databases.

Main Methods:

  • Iterative construction of a hidden Markov model (HMM) starting from a single target sequence.

Related Experiment Videos

  • Database searching using the iteratively refined HMM.
  • Evaluation using fold-recognition tests based on structural similarity and a curated database.
  • Comparison with WU-BLASTP and DOUBLE-BLAST.

    • Main Results:

    • SAM-T98 achieved the lowest error rates across all evaluated datasets, particularly excelling in fold-recognition tasks.
    • On the SCOP-domains test, SAM-T98 identified 880 true positives with 68 false positives, significantly outperforming DOUBLE-BLAST (533 true positives, 71 false positives) and WU-BLASTP (353 true positives, 24 false positives).
    • A novel score-normalization technique comparing scores against a reversed model proved key to SAM-T98's enhanced performance.

    Conclusions:

    • SAM-T98 represents a significant advancement in sensitive protein homology detection, especially for identifying distant evolutionary relationships.
    • The method is optimized for superfamily recognition and may require parameter tuning for family or fold-level searches.
    • The Sequence Alignment and Modeling (SAM) software suite, including SAM-T98, is publicly available.