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Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships

S E Brenner1, C Chothia, T J Hubbard

  • 1MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, United Kingdom. brenner@hyper.stanford.edu

Proceedings of the National Academy of Sciences of the United States of America
|May 30, 1998
PubMed
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Statistical scores significantly improve protein sequence comparison accuracy. SSEARCH and FASTA E-values are reliable, unlike BLAST and WU-BLAST2 P-values, enhancing protein relationship detection.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Protein sequence comparison is crucial for understanding protein function and evolution.
  • Reliable assessment of pairwise sequence comparison methods requires well-characterized protein relationships.
  • The Structural Classification of Proteins (SCOP) database provides a reliable benchmark for protein relationships based on structure and function.

Purpose of the Study:

  • To evaluate the performance of common pairwise sequence comparison algorithms and their scoring schemes.
  • To determine the accuracy of statistical scores in assessing protein sequence similarity.
  • To identify the most effective methods for detecting evolutionary relationships between proteins.

Main Methods:

  • Assessed protein sequence comparison programs: BLAST, WU-BLAST2, FASTA, and SSEARCH.

Related Experiment Videos

  • Utilized proteins with known relationships from the SCOP database for evaluation.
  • Compared performance based on statistical scores (E-value, P-value) versus raw scores and percentage identity.

    • Main Results:

    • Using statistical scores significantly reduces the error rate of all tested algorithms.
    • SSEARCH and FASTA E-values accurately reflect the number of false positives.
    • BLAST and WU-BLAST2 P-values overestimate significance.
    • SSEARCH, FASTA (ktup=1), and WU-BLAST2 perform best, detecting most relationships >30% sequence identity.
    • Performance decreases for more distant relationships (20-30% identity), with only half detected.
    • Many distant homologs with low sequence similarity remain undetectable by pairwise methods.

    Conclusions:

    • Statistical scores, particularly E-values from SSEARCH and FASTA, are more reliable for evaluating protein sequence similarity than raw scores or percentage identity.
    • While current pairwise methods are effective for closely related proteins, they have limitations in detecting distant evolutionary relationships.
    • Identified relationships using these methods can be confidently used, but the inability to detect all distant homologs highlights the need for complementary approaches.