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Related Experiment Videos

Using the Structure-function Linkage Database to characterize functional domains in enzymes.

Shoshana Brown1, Patricia Babbitt

  • 1University of California, San Francisco, San Francisco, California, USA.

Current Protocols in Bioinformatics
|April 23, 2008
PubMed
Summary
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The Structure-Function Linkage Database (SFLD) aids in predicting enzyme functions from sequences and correcting existing annotations. It is particularly useful for enzymes distantly related to known ones.

Area of Science:

  • Biochemistry
  • Bioinformatics
  • Enzymology

Background:

  • Enzyme function prediction is crucial for biological research.
  • Existing databases may contain misannotated enzyme functional assignments.
  • Linking enzyme sequence, structure, and function is a key challenge.

Purpose of the Study:

  • To describe protocols for querying the Structure-Function Linkage Database (SFLD).
  • To enable prediction of functions for newly sequenced enzymes.
  • To facilitate correction of misannotated enzyme functions in public databases.

Main Methods:

  • Web-accessible database querying.
  • Utilizing enzyme sequence and structure information.
  • Comparison with characterized enzyme sequences.

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Main Results:

  • SFLD provides a method to link enzyme sequence, structure, and function.
  • Protocols allow for prediction of novel enzyme functions.
  • Database aids in correcting erroneous functional assignments.

Conclusions:

  • The SFLD is a valuable resource for enzyme function prediction and annotation refinement.
  • It is especially beneficial for analyzing enzymes with distant sequence homology.
  • This tool enhances the accuracy of functional assignments in public databases.