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Related Experiment Videos

Highly specific protein sequence motifs for genome analysis

C G Nevill-Manning1, T D Wu, D L Brutlag

  • 1Department of Biochemistry, Stanford University, Stanford, CA 94305-5307, USA.

Proceedings of the National Academy of Sciences of the United States of America
|May 30, 1998
PubMed
Summary
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A new computational method, EMOTIF, discovers conserved protein sequence motifs. The resulting IDENTIFY database aids in assigning biological functions to proteins, including those of unknown function in yeast.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying conserved sequence motifs is crucial for understanding protein function and evolution.
  • Existing methods may lack the sensitivity or specificity required for comprehensive analysis of large protein families.

Purpose of the Study:

  • To develop and present a novel computational method, EMOTIF, for discovering conserved sequence motifs from aligned protein sequences.
  • To create a comprehensive database, IDENTIFY, of these motifs for broad biological applications.
  • To assess the utility of EMOTIF-derived motifs in assigning biological functions to proteins across different genomes.

Main Methods:

  • Implementation of the EMOTIF computer program for motif discovery from aligned protein sequences.
  • Generation of motifs with varying specificities and sensitivities, including those representing subfamilies.

Related Experiment Videos

  • Construction of the IDENTIFY database by applying EMOTIF to over 7,000 protein alignments from BLOCKS and PRINTS databases.
  • Evaluation of motif performance based on low false positive rates (10^-10 to 10^-5).

    • Main Results:

    • EMOTIF successfully generates diverse motifs, enabling representation of protein superfamilies with high specificity and sensitivity.
    • The IDENTIFY database contains over 50,000 motifs derived from extensive protein alignment datasets.
    • Highly specific motifs are suitable for proteome-wide searches with minimal false predictions.
    • IDENTIFY assigned functions to 25-30% of proteins in yeast and bacterial genomes, including 172 previously uncharacterized yeast proteins.

    Conclusions:

    • EMOTIF provides a powerful tool for discovering conserved protein sequence motifs and subfamily patterns.
    • The IDENTIFY database represents a valuable resource for functional annotation of proteins.
    • This approach significantly contributes to the understanding of proteomes and the identification of novel protein functions.