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Related Experiment Videos

Comparison of sequence profiles. Strategies for structural predictions using sequence information.

L Rychlewski1, L Jaroszewski, W Li

  • 1San Diego Supercomputer Center, La Jolla, California 92093, USA.

Protein Science : a Publication of the Protein Society
|March 15, 2000
PubMed
Summary

New protein alignment tools struggle to identify distant homologies, even with profile-based methods. While functional similarities improve, recognizing distant fold similarities remains a challenge for algorithms like PSI-BLAST and BASIC.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural biology

Background:

  • Detecting distant protein homologies is challenging due to significant sequence divergence.
  • Three-dimensional structures are often required for identification, limiting large-scale analysis.
  • Profile-based alignment tools offer improved sensitivity over sequence comparison.

Purpose of the Study:

  • To compare the performance of new sensitive alignment tools in detecting distant protein homologies.
  • To evaluate algorithms like BLAST, PSI-BLAST, and BASIC on structurally similar proteins with low sequence similarity.
  • To introduce and assess a novel algorithm, FFAS, for profile generation and homology detection.

Main Methods:

  • Benchmarking several profile-based alignment algorithms, including new BLAST generations, BASIC, and FFAS.

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  • Utilizing a large dataset of structurally similar proteins with minimal sequence similarity.
  • Classifying proteins by similarity levels to analyze algorithm performance across different homology strengths.

    • Main Results:

    • New algorithms show improvement primarily for proteins with strong functional similarities, not distant fold similarities.
    • Profile calculation details significantly impact sensitivity; divergent family member inclusion is crucial.
    • PSI-BLAST offers reliability with conservative profiling; BASIC achieves higher sensitivity at the cost of reliability.

    Conclusions:

    • The FFAS algorithm demonstrates comparable sensitivity to BASIC and reliability to PSI-BLAST.
    • FFAS utilizes a novel profile generation procedure considering all family member relations.
    • Accurate profile generation is key to balancing sensitivity and reliability in distant homology detection.