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Related Experiment Videos

A unified statistical framework for sequence comparison and structure comparison

M Levitt1, M Gerstein

  • 1Department of Structural Biology, Stanford University, Stanford, CA 94305, USA. levitt@stanford.edu

Proceedings of the National Academy of Sciences of the United States of America
|May 30, 1998
PubMed
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This study introduces a unified statistical method for comparing protein sequences and structures, enabling accurate significance (P value) assignment. Structural comparisons detect twice as many distant protein relationships as sequence comparisons at equivalent error rates.

Area of Science:

  • Bioinformatics
  • Structural Biology
  • Computational Biology

Background:

  • Assessing the statistical significance of sequence and structure comparisons is crucial for understanding protein relationships.
  • Existing methods often use different statistical formalisms for sequence and structure comparisons, potentially limiting direct comparability.
  • Protein domain comparisons are fundamental to evolutionary and functional analyses.

Purpose of the Study:

  • To develop a unified statistical approach for assessing the significance of both protein sequence and structure comparisons.
  • To enable the calculation of P values for comparison scores, indicating the probability of observing a better score by chance.
  • To compare the efficacy of sequence versus structure comparison in detecting distant protein relationships.

Main Methods:

Related Experiment Videos

  • Performed all-vs-all comparisons of protein domains from the Structural Classification of Proteins (SCOP) database.
  • Fitted distribution functions to observed comparison scores to derive statistical significance (P values).
  • Utilized an extreme-value distribution for sequence comparison scores and a structural alignment score for structure comparisons.

Main Results:

  • Sequence comparison scores followed an extreme-value distribution, validating the approach against standard tools like BLAST and FASTA.
  • Structure comparison scores, using a structural alignment score, also followed an extreme-value distribution.
  • Structural comparison detected approximately twice as many distant protein relationships as sequence comparison at the same error rate, with many significant structure similarities lacking sequence similarity.

Conclusions:

  • The unified statistical formalism provides a robust method for assigning significance to both sequence and structure comparison scores.
  • Structural comparison is more sensitive than sequence comparison for detecting distantly related proteins.
  • The findings highlight the importance of considering structural information, as it reveals relationships missed by sequence-based methods alone.