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Effective protein sequence comparison

W R Pearson1

  • 1Department of Biochemistry, University of Virginia, Charlonesville 22908, USA.

Methods in Enzymology
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

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This study outlines a robust protocol for identifying homologous sequences using protein comparison tools. Following these steps minimizes false positives, ensuring reliable detection of evolutionary relationships.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Evolution

Background:

  • Multiple sequence comparison programs (e.g., BLASTP, FASTA) and scoring systems (e.g., BLOSUM62) exist for analyzing homologous sequences.
  • Accurate identification of homologous sequences is crucial for understanding evolutionary relationships and protein function.
  • Existing methods may have limitations in sensitivity or specificity, necessitating a refined search protocol.

Purpose of the Study:

  • To establish a comprehensive search protocol for reliably identifying homologous sequences.
  • To provide criteria for evaluating the statistical significance of sequence similarity scores.
  • To minimize the misidentification of unrelated sequences as homologous.

Main Methods:

  • Prioritize protein sequence comparison over DNA comparison to extend evolutionary detection.

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  • Employ rapid comparison programs (e.g., BLASTP, FASTA) across multiple databases (e.g., PIR, SWISS-PROT, GenPept).
  • Utilize statistical thresholds (e.g., FASTA E-values < 0.02) and secondary validation (e.g., SSEARCH, PRSS with 1000 shuffles) for homology confirmation.
  • Consider sequence identity (20-25% over >200 residues) and alignment length for robust homology inference.

    • Main Results:

    • A systematic approach using specific search parameters and statistical cutoffs significantly enhances the accuracy of homology detection.
    • Thresholds for accepting sequences as homologous are defined, with lower E-values indicating higher confidence.
    • The protocol balances sensitivity for detecting distant relationships with stringency to avoid false positives.

    Conclusions:

    • The proposed protocol effectively identifies homologous sequences by combining rapid database searches with rigorous statistical validation.
    • Adherence to these criteria minimizes the likelihood of incorrectly asserting homology between unrelated sequences.
    • While stringent, the protocol aims to maximize reliable discoveries, acknowledging a potential trade-off in detecting very distant homologs.