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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

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Published on: November 3, 2011

Quality measures for protein alignment benchmarks.

Robert C Edgar1

  • 1bob@drive5.com

Nucleic Acids Research
|January 6, 2010
PubMed
Summary
This summary is machine-generated.

Evaluating protein alignment benchmarks reveals significant issues. New quality measures show many benchmarks, especially BALIBASE, contain questionable alignments and redundancy, impacting reliable algorithm ranking.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Multiple protein sequence alignment is crucial for molecular biology.
  • Existing benchmark datasets (e.g., BALIBASE, OXBENCH) are used to evaluate alignment methods.
  • Reliable benchmarks are essential for biologists to choose appropriate alignment programs.

Purpose of the Study:

  • To define new measures of alignment quality using domain homology and secondary structure annotations.
  • To conduct the first systematic, independent evaluation of current protein multiple alignment benchmarks.
  • To assess the reliability of existing benchmarks for ranking protein alignment algorithms.

Main Methods:

  • Developed novel alignment quality measures based on domain homology and secondary structure.
  • Performed a systematic evaluation of benchmark datasets (BALIBASE, OXBENCH, PREFAB, SABMARK).
  • Assessed alignments from selected methods (ALIGN-M, DIALIGN-T, FSA, MUSCLE) using high-confidence columns.

Main Results:

  • Identified significant redundancy in most benchmarks, except SABMARK.
  • Found questionable alignments within benchmarks, particularly BALIBASE, with issues in sequence structure, fold consistency, and secondary structure.
  • New measures demonstrated sensitivity and specificity, avoiding limitations of structural superpositions.

Conclusions:

  • Current protein multiple alignment benchmarks exhibit limitations affecting their ability to reliably rank algorithms.
  • The systematic evaluation highlights the need for improved benchmark datasets and quality assessment methods.
  • Findings question the validity of current benchmark-based algorithm rankings in protein sequence alignment.