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The EMOTIF database.

J Y Huang1, D L Brutlag

  • 1Department of Biochemistry, Stanford University, Stanford, CA 94305-5307, USA. yhuang@leland.stanford.edu

Nucleic Acids Research
|January 11, 2000
PubMed
Summary
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The EMOTIF database offers over 170,000 protein sequence motifs for identifying conserved biological functions. These motifs enable broader sequence classification compared to traditional similarity methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Protein sequence analysis is crucial for understanding biological functions.
  • Existing methods often struggle with classifying divergent sequences.
  • Conserved sequence patterns (motifs) hold significant biochemical and functional information.

Purpose of the Study:

  • To introduce the EMOTIF database, a comprehensive resource for protein sequence motifs.
  • To highlight the utility of EMOTIF patterns for enhanced sequence classification.

Main Methods:

  • Developed the emotif-maker algorithm for motif extraction.
  • Integrated data from BLOCKS+ and PRINTS databases.
  • Compiled over 170,000 protein sequence motifs.

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Main Results:

  • The EMOTIF database contains >170,000 specific and sensitive protein motifs.
  • Motifs represent conserved biochemical properties and biological functions.
  • EMOTIF patterns allow identification of more divergent sequences than global similarity.

Conclusions:

  • EMOTIF provides a powerful tool for protein sequence analysis.
  • The database facilitates broader classification of protein sequences.
  • It enhances the discovery of conserved functional elements in proteins.